Graphical user interface for visual correlation of virtual machine information and storage volume information

ABSTRACT

The disclosed embodiments include a method for identifying a performance metric to diagnose a cause of a performance issues of virtual machine. The method includes obtaining data of a virtual machine, an indication that a storage volume contains data of the virtual machine, data about the storage volume, and an identification of the storage volume. The data of the virtual machine is correlated with the data about the storage volume based on the indication that the storage volume contains data of the virtual machine and the identification of the storage volume. A performance metric is identified based at least in part on an outcome of the correlating. The performance metric indicates that the storage volume is a cause of a performance issue of the virtual machine. A state related to the storage volume is changed to mitigate the cause of the performance issue of the virtual machine.

The present application is a continuation of U.S. patent applicationSer. No. 15/981,745 filed on May 16, 2018 entitled “PERFORMANCE METRICSFOR DIAGNOSING CAUSES OF POOR PERFORMING VIRTUAL MACHINES”, which is acontinuation of U.S. patent application Ser. No. 14/688,040 filed onApr. 16, 2015, entitled “DIAGNOSING CAUSES OF PERFORMANCE ISSUES OFVIRTUAL MACHINES”, which issued as U.S. Pat. No. 9,990,265, which is acontinuation of U.S. patent application Ser. No. 14/253,795 filed onApr. 15, 2014, entitled “CORRELATION AND ASSOCIATED DISPLAY OF VIRTUALMACHINE DATA AND STORAGE PERFORMANCE DATA”, which issued as U.S. Pat.No. 9,052,938, each of which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The disclosure generally relates to determining and presentinginformation relating to virtual machines and the performance of volumesused by the virtual machines for storage.

BACKGROUND

In a virtualized environment, a large number of virtual machines canoperate on a single physical host. Many customers elect to use virtualmachines for their data computing needs due to the various advantagesthat a virtualized environment can offer over a non-virtualizedenvironment, such as greater availability, lower costs, and simplerupgrades. When a virtual machine is created, the physical host allocatesresources such as central processing units (CPUs) and memory to thevirtual machine. For disk space, the virtual machine may use the storageresources of a storage provider that is different from the physical hostthat provides CPU and memory resources to the virtual machine. Forexample, the data generated and used by the virtual machine may bestored in volumes managed by a storage controller, such as a filer thatoperates on a separate machine than the physical host of the virtualmachine and uses software provided by a different vendor than the vendorof the virtualization software.

A performance issue originating in the storage environment, such as aproblem affecting a particular volume or a storage controller thatmanages the particular volume, can affect the performance of the virtualmachine that utilizes the particular volume. However, in other cases thepoor performance of a virtual machine can be attributed to a differentsource; for example, the problem may be specific to the virtual machineitself, to the communication network used by the virtual machine, theunderlying physical host, or some other entity. Many times it isdifficult for customers to pinpoint the source of the problem. Betterapproaches for presenting information to assist a customer in diagnosingthe source of a problem that affects a virtual machine are needed.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 illustrates an example network-based system of computing devicesin which the described techniques may be practiced, according to anembodiment.

FIG. 2 illustrates an example process for the collection, correlation,and display of data from a virtual system manager and a storagecontroller.

FIG. 3 illustrates an example process for the display of virtual machineinformation relating to a particular virtual machine in association withvolume performance information.

FIG. 4 illustrates an example graphical interface that includesperformance information relating to the virtual machine and anidentification of a volume associated with the virtual machine.

FIGS. 5A and 5B illustrate an example graphical interface that includesinformation about a particular volume.

FIG. 6 is a block diagram that illustrates a computer system upon whichembodiments of the invention may be implemented.

FIG. 7 illustrates an example block diagram of a data intake and querysystem, according to an embodiment of the invention.

FIG. 8 illustrates a flowchart of a process that indexers may use toprocess, index, and store data received from forwarders, according to anembodiment of the invention.

FIG. 9 illustrates a flowchart of a process that a search head andindexers perform during a typical search query, according to anembodiment of the invention.

FIG. 10 illustrates an example of a search query received from a clientthat the search head can split into two parts, according to anembodiment of the invention.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Example embodiments, which relate to correlation and associated displayof virtual machine data and storage performance data, are describedherein. In the following description, for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

Embodiments are described herein according to the following outline:

-   -   1.0 General Overview    -   2.0 Operating Environment    -   3.0 Example Collection, Correlation, and Display Processes    -   4.0 Implementation Mechanisms—Hardware Overview    -   5.0 Extensions and Alternatives

1. General Overview

This overview presents a basic description of some aspects ofembodiment(s) of the present invention. It should be noted that thisoverview is not an extensive or exhaustive summary of aspects of theembodiment. Moreover, it should be noted that this overview is notintended to be understood as identifying any particularly significantaspects or elements of the embodiment(s), nor as delineating any scopeof the embodiment(s) in particular, nor the invention in general. Thisoverview merely presents some concepts that relate to exampleembodiments in a condensed and simplified format, and should beunderstood as merely a conceptual prelude to a more detailed descriptionof example embodiments that follows below.

In an embodiment, a data management system obtains data from both avirtual system manager that manages the virtualized environment and astorage controller that manages the storage of information in one ormore disks. The virtual system manager may provide a variety ofinformation relating to the virtual machines managed by the virtualmachine manager or the physical hosts of the virtual machines including,but not limited to, names of the entities in the virtualizedenvironment, changes in the configuration of various virtual machines,information relating to migrations of the virtual machines, amount ofmemory or CPU resources assigned to the virtual machines, performance ofthe virtual machines, an identification of volumes that the virtualmachines are configured to use, and the amount of storage space utilizedin the volumes by various virtual machines,

Similarly, the storage controller may provide a variety of informationrelating to the entities managed by the storage controller or thestorage controller itself, such as the virtual machines managed by thestorage controller, the names of and hierarchy between differententities of the storage environment, and performance of the volumes ordisks managed by the storage controller or the storage controlleritself.

Records obtained from the virtual system manager and records obtainedfrom the storage controller may be correlated. For example, based on adetermination that one or more particular storage data records relate toa particular volume used by a particular virtual machine to which one ormore particular virtual machine data records relate, the data managementsystem may correlate the one or more particular storage data recordswith the one or more particular virtual machine data records.

Based on the correlated data records, the data management system maydisplay virtual machine data in association with storage data, in oneembodiment, performance information for a particular virtual machine isdisplayed on a first screen and, in response to a user selection,performance information specifically for the volume that the particularvirtual machine uses is displayed on the next screen, within the sameapplication.

In another embodiment, the data management system displays an interfacethat identifies the capacity of a volume, the path that a virtualmachine uses to access the volume, and performance information for theparticular volume. The capacity of the volume, the path that a virtualmachine uses to access the volume, and the performance of the virtualmachine may be determined based on records obtained from the virtualsystem manager and the volume performance information may be identifiedbased on records obtained from the storage controller.

The associated display of virtual machine data and the storage data mayallow a user to more easily diagnose the root cause of a performanceissue affecting a virtual machine.

Various modifications to the preferred embodiments and the genericprinciples and features described herein will be readily apparent tothose skilled in the art. Thus, the disclosure is not intended to belimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features described herein.

Other embodiments include, without limitation, a non-transitorycomputer-readable medium that includes processor-executable instructionsthat enable a processing unit to implement one or more aspects of thedisclosed methods as well as a system configured to implement one ormore aspects of the disclosed methods.

2. Operating Environment

There is tremendous growth in the amount of data generated in the world.With decreasing storage costs and seemingly infinite capacity due tocloud services, there are fewer reasons to discard old data, and manyreasons to keep it. As a result, challenges have shifted towardsextracting useful information from massive quantities of data.

Mining a massive dataset is non-trivial but a more challenging task isto cross-correlate and mine multiple datasets from various sources. Forexample, a datacenter monitors data from thousands of components; thelog format and collection granularities vary by component type andgeneration. The only underlying assumption that can be made is that eachcomponent has a notion of time, either via timestamps or eventsequences, that is captured in the logs. As the quantity and diversityof data grow, there is an increasing need for performing full textsearches to mine the data.

Another challenge is that a large fraction of the world's data isunstructured, making it difficult to index and query using traditionaldatabases. Even if a dataset is structured, the specifics of thestructure may evolve with time, for example, as a consequence of systemupgrades or more/less restrictive data collection/retention policies.

SPLUNK® ENTERPRISE is software produced and sold for on-premise andcloud use by Splunk Inc. of San Francisco, Calif. SPLUNK ENTERPRISE is acomprehensive system that generates, stores, retrieves, and searchesevent data. SPLUNK® ENTERPRISE has gained particular appeal in themarket for deriving events from unstructured data and machine data. Itis the leading software for providing real-time operationalintelligence, enabling organizations to collect, index, and harnessmachine-generated big data coming from the websites, applications,servers, networks, mobile devices, etc., that power their businesses.

At a high level, SPLUNK® ENTERPRISE can take raw data, unstructureddata, or machine data such as data in Web logs, syslogs, sensorreadings, etc., divide the data up into portions, and optionallytransform at least part of the data in these portions to producetime-stamped events. The software derives the time stamp for each eventby extracting it from the event data itself or by interpolating anevent's time stamp relative to other events for which the software canderive a time stamp. SPLUNK® ENTERPRISE then stores the events in atime-series data store against which it can run queries to retrieveevents that meet specified criteria, such as having certain keywordsand/or having certain value(s) for certain defined field(s).

SPLUNK® ENTERPRISE is particularly noteworthy for employing a so-called“late-binding schema.” As noted, an event in SPLUNK® ENTERPRISEtypically contains a portion of raw data (or a transformed version ofsuch). To run queries against events other than those involving keywordsearches, a schema can be developed. Such a schema can includeextraction rules for one or more fields. Each field can be defined for asubset of the events in the data store and an extraction rule canspecify how to extract a value from each of the subset of events forwhich the field has been defined. The extraction rule for a field isoften defined using a regular expression (“regex” rule), and itassociates event data with a logical type of information that iscontained within an event for which it is defined. The term“late-binding schema” refers to a system, such as in SPLUNK® ENTERPRISE,which does not define the schema at index time as with databasetechnology; rather, in a system involving late-binding schema, theschema can be developed on an ongoing basis up until the time it needsto be applied (which is query time, as a query often specifies thecriteria for events of interest in terms of events having specifiedvalue(s) for specified field(s)). As a data analyst learns more aboutthe data in stored events, using a late-binding schema, he can continueto develop the schema up until the next time it is needed for a query.

Because SPLUNK® ENTERPRISE maintains the underlying searchable raw dataand enables application of a late-binding schema, it has great power toenable dynamic investigation of issues that arise as a data analystlearns more about the data stored in the system's events.

As discussed herein, “time-series data” and “time-series machine data”may include, among other things, a series or sequence of data pointsgenerated by one or more data sources, computing devices, or sensors.Each data point may be a value, a small segment of data, or a largesegment of data, and each data point may be associated with a timestampor be associated with a particular point in time that provides the basisfor a timestamp for the data point. The series of data points, orvalues/statistics derived from the data points, may be plotted over atime range or time axis representing at least a portion of the timerange. The data can be structured, unstructured, or semi-structured andcan come from files, directories, network packets, network events,and/or sensors. Unstructured data may refer, for example, to data whosestructure is not fully understood or appreciated at the time the data isobtained by a data storage system, or it may refer to data that wasgenerated without a particular schema in mind to facilitate theextraction of values for fields in the data during a search on the data.Machine data generated by, for example, data sources within anenterprise network environment is generally considered to beunstructured data. The visualization of such time-series data may beused to display statistical trends over time. The time-series machinedata collected from a data source may be segmented or otherwisetransformed into discrete events, where each event can be associatedwith a timestamp.

An “event” may include a single record of activity from a particulardata source associated with a single timestamp. Such an event maycorrespond to, for example, one or more lines in a log file or otherdata input. Further, “events” may be derived from processing or indexingmachine data, as described herein, or may include other kinds of eventsor notable events described herein. Events can also correspond to anytime-series data, such as performance measurements of an IT component(e.g., a computer cluster, node, host, virtual machine, etc.), a sensormeasurement, etc.

In an example, a field extractor within an enterprise networkenvironment may be configured to automatically identify (e.g., usingregular expression-based rules, delimiter-based rules, etc.) certainfields in the events while the events are being created, indexed, and/orstored. Alternatively, one or more fields can be identified within theevents and added to the field extraction rules (used by the fieldextractor to identify fields within the events) by a user using avariety of techniques. Additionally, fields that correspond to metadataabout the events, such as a timestamp, host, source, and source type foran event, may also be created; such fields may, in some cases, bereferred to as “default fields” if they are determined automatically forall events at the time such events are created, indexed, and/or stored.

In some implementations, a given tag or alias may be assigned to a setof two or more fields to identify multiple fields that correspond toequivalent pieces of information, even though those fields may havedifferent names or be defined for different sets of events. A set oftags or aliases used to identify equivalent fields in this way may bereferred to as a common information model.

Data generated by various data sources may be collected and segmentedinto discrete events, each event corresponding to data from a particularpoint in time. Examples of such data sources include, but are notlimited to, web servers, application servers, databases, firewalls,routers, operating systems, software applications executable at one ormore computing devices within the enterprise data system, mobiledevices, sensors, etc. The types of data generated by such data sourcesmay be in various forms including, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements or metrics, sensormeasurements, etc.

FIG. 7 shows a block diagram of SPLUNK® ENTERPRISE's data intake andquery system, which provides an example embodiment of a data intake andquery system 700. Generally, the system 700 includes one or moreforwarders 701 that collect data from a variety of different datasources 705. The forwarders determine which indexer or indexers are toreceive the data and forward the data to one or more indexers 702. Thedata typically includes streams of time-series data. Time-series datarefers to any data that can be segmented such that each segment can beassociated with a time stamp. The data can be structured, unstructured,or semi-structured and can come from files and directories. Unstructureddata is data that is not organized to facilitate the extraction ofvalues for fields from the data, as is often the case with machine dataand web logs, two popular data sources for SPLUNK® ENTERPRISE.Alternatively, heavy forwarders can strip out extraneous data and detecttime stamps for the data. Based on the time stamps, the heavy forwarderscan index and group the data into buckets that fall within a common timespan. The heavy forwarders then determine which indexer or indexers areto receive each bucket of data and forward the data to one or moreindexers 702.

FIG. 8 is a flowchart 800 of a process that indexers 702 may use toprocess, index, and store data received from the forwarders 701. Atblock 801, an indexer 702 receives data from a forwarder 701. At block802, the indexer segments the data into events. The data typicallyconsists of many lines of text that are separated by a carriage returnor line break. An event may consist of one or more of these lines. Thetask of the indexer is to determine where an event begins and ends inthe lines of data. The indexer can use heuristics that allow it toautomatically determine how many lines constitute an event. The indexermay be informed of the source of the data and have a set of heuristicrules for the source. The indexer may also be able to examine a samplingof the data and automatically determine the source of the data and havea set of heuristic rules for that source. These heuristics allow theindexer to use regular expression-based rules, delimiter-based rules,etc., to examine the text in each line in order to combine lines of datato form an event. The indexer can examine the text for event boundarieswithin the text that include, but are not limited to: predefinedcharacters, character strings, etc. These may include certainpunctuation marks or special characters including, for example, carriagereturns, tabs, spaces, line breaks, etc. In some instances, a user canfine tune or configure the rules that the indexers use to examine thetext in order to adapt to the user's equipment.

The indexer determines a time stamp for each event at block 803. Thetime stamp can be determined by extracting the time from data in theevent or by interpolating the time based on time stamps from otherevents. In some cases, a time stamp can be determined from the time thedata was received or generated. The indexer associates the time stampwith each event at block 804. For example, the time stamp may be storedas metadata for the event.

At block 805, the data included in a given event can be transformed.Such a transformation can include such actions as removing part of anevent (e.g., a portion used to define event boundaries, extraneous text,characters, etc.) or removing redundant portions of an event. A user canspecify a portion to remove using a regular expression or any similarmethod.

Optionally, a key word index can be built to facilitate fast keywordsearching of events. To build such an index, in block 806, the indexeridentifies a set of keywords contained in the events. At block 807, theindexer includes each identified keyword in an index, which associateswith each stored keyword pointers to each event containing that keyword(or locations within events where that keyword is found). When anindexer receives a keyword-based query, the indexer can then consultthis index to quickly find those events containing the keyword withouthaving to examine again each individual event, thereby greatlyaccelerating keyword searches.

The indexer stores events in a data store at block 808. The data can bestored in working, short-term and/or long-term memory in a mannerretrievable by query. The time stamp can be stored along with each eventto help optimize searching the events by time range.

In some instances, the stored data includes a plurality of individualstorage buckets, each corresponding to a time range. An event can thenbe stored in a bucket associated with a time range inclusive of theevent's time stamp. This not only optimizes time based searches, but itcan allow events with recent time stamps that may have a higherlikelihood of being accessed to be stored at preferable memory locationsthat lend to quicker subsequent retrieval (such as flash memory insteadof hard disk media).

Data stores 703 may be distributed across multiple indexers, eachresponsible for storing and searching a subset, or buckets, of theevents generated by the system. By distributing the time-based bucketsamong the indexers, the indexers can find events responsive to a queryin parallel using map-reduce techniques, each returning their partialresponses for specific buckets to the query to a search head thatcombines the results together to answer the query.

FIG. 9 is a flowchart 900 of a process that a search head 704 andindexers 702 may perform during a typical search query. At block 901, asearch head receives a query from a client.

At block 902, the search head is responsible for analyzing the searchquery to determine what part can be delegated for execution by indexersand what part needs to be executed by the search head. Streamingcommands can be trivially delegated to the indexers. Conversely,aggregating commands are more complex to distribute.

The search head can perform optimization steps in order to make thesearch more efficient. As mentioned above, the indexers may create anindex of keywords. In one optimization, before the search startsexecuting, the search head determines the time range required for thesearch and a set of common keywords that all matching events must have.The retrieval phase uses these parameters to query the indexers for asuperset of the eventual results. The indexers return the superset ofresults that the search head can perform a filtering stage on. Thefiltering stage performs field extraction on the superset to arrive at areduced set of search results.

In another optimization, to achieve better computation distribution andminimize the amount of data transferred between indexers and the searchhead, many aggregating commands implement a map operation which thesearch head can delegate to the indexers while executing the reduceoperation locally. FIG. 10 shows an example of a search query 1001received from a client that the search head can split into two parts:one part to be executed by indexers 1002 and one part to be executed bythe search head 1003. Here, the search query 1002 makes the indexersresponsible for counting the results by host and then sending theirresults to the search head. The search head then performs the merging1003. This achieves both computation distribution and minimal datatransfer.

The search head distributes the indexer search query to one or moredistributed indexers. The search query may contain one or more regularexpressions that the indexer is to apply to any event data that is foundto fall within the parameters of the regular expression. These indexerscan include those with access to data stores having events responsive tothe query. For example, the indexers can include those with access toevents with time stamps within part or all of a time period identifiedin the query.

At block 903, one or more indexers to which the query was distributedsearches its data store for events responsive to the query. To determineevents responsive to the query, a searching indexer finds eventsspecified by the criteria in the query. This criteria can include thatthe events have particular keywords or contain a specified value orvalues for a specified field or fields (because this employs alate-binding schema, extraction of values from events to determine thosethat meet the specified criteria occurs at the time this query isprocessed). It should be appreciated that, to achieve high availabilityand to provide for disaster recovery, events may be replicated inmultiple data stores, in which case indexers with access to theredundant events and not assigned as the primary indexer for the events,would not respond to the query by processing the redundant events. In anexample, the indexer finds events that it is the primary indexer forthat fall within a block of time specified by the one or more regularexpressions. The indexer then processes the contents of the events usingthe one or more regular expressions, extracting information associatedwith fields specified in the one or more regular expressions. Theindexers can either stream the relevant events back to the search heador use the events to calculate a partial result responsive to the queryand send the partial result back to the search head. At block 904, thesearch head combines or reduces all of the partial results or eventsreceived from the parallel processing indexers together to determine afinal result responsive to the query.

Data intake and query system 700 and the processes described withrespect to FIGS. 1-4 are further discussed and elaborated upon inCarasso, David. Exploring Splunk Search Processing Language (SPL) Primerand Cookbook, New York: CITO Research, 2012 and in Ledion Bitincka,Archana Ganapathi, Stephen Sorkin, and Steve Zhang. Optimizing dataanalysis with a semi-structured time series database. In SLAML, 8070.Each of these references is hereby incorporated by reference in itsentirety for all purposes.

SPLUNK® ENTERPRISE can accelerate some queries used to periodicallygenerate reports that, upon each subsequent execution, are intended toinclude updated data. To accelerate such reports, a summarization engineperiodically generates a summary of data responsive to the querydefining the report for a defined, non-overlapping subset of the timeperiod covered by the report. For example, where the query is meant toidentify events meeting specified criteria, a summary for a given timeperiod may include only those events meeting the criteria. Likewise, ifthe query is for a statistic calculated from events, such as the numberof events meeting certain criteria, then a summary for a given timeperiod may be the number of events in that period meeting the criteria.

Because the report, whenever it is run, includes older time periods, asummary for an older time period can save the work of having to re-runthe query on a time period for which a summary was generated, so onlythe newer data needs to be accounted for. Summaries of historical timeperiods may also be accumulated to save the work of re-running the queryon each historical time period whenever the report is updated.

A process for generating such a summary or report can begin byperiodically repeating a query used to define a report. The repeatedquery performance may focus on recent events. The summarization enginedetermines automatically from the query whether generation of updatedreports can be accelerated by creating intermediate summaries for pasttime periods. If it can, then a summarization engine can periodicallycreate a non-overlapping intermediate summary covering new data obtainedduring a recent, non-overlapping time period and stores the summary in asummary data store.

In parallel to the creation of the summaries, the query engine schedulesthe periodic updating of the report defined by the query. At eachscheduled report update, the query engine determines whetherintermediate summaries have been generated covering parts of the timeperiod covered by the current report update. If such summaries exist,then the report is based on the information from the summaries;optionally, if additional data has been received that has not yet beensummarized but that is required to generate a complete report, then thequery is run on this data and, together with the data from theintermediate summaries, the updated current report is generated. Thisprocess repeats each time an updated report is scheduled for creation.

Search and report acceleration methods are described in U.S. Pat. No.8,589,403, issued on Nov. 19, 2013, and U.S. Pat. No. 8,412,696, issuedon Apr. 2, 2011, both of which are hereby incorporated by reference intheir entirety for all purposes.

The data processing techniques described herein are suitable for use bysystems deployed in a variety of operating environments. FIG. 1illustrates an example network-based system 100 of computing devices inwhich the described techniques may be practiced, according to anembodiment.

Data management system 102 represents one or more computing devices thatmay collect, index, and correlate data from both virtual system manager112 and storage controller 130.

VM data collection node 110 obtains data relating to virtual environment152 from virtual system manager 112. In an embodiment, VM datacollection node 110 collects the data by making calls to an ApplicationProgram Interface (API) made available by virtual system manager 112.

Virtual environment 152 comprises virtual machine manager 114, whichmanages virtual machines 116 and 118, and virtual machine manager 120,which manages virtual machines 122 and 124. Virtual machine managers 114and 120 may be hypervisors that provide services such as allocation andpartitioning, which allow the respective virtual machines that theymanage to share the same physical host. Virtual system manager 112manages the virtual machine managers and virtual machines in virtualenvironment 152 by providing services such as configuration of virtualmachine managers and virtual machines, performance monitoring, andoptimization of resource usage. Virtual system manager 112 may operateon a virtual machine within virtual environment 152 or on a physical orvirtual machine outside virtual environment 152. VM data collection node110 may be configured to re-structure or otherwise modify the dataobtained from virtual system manager 112 to conform to a particularformat before forwarding the data to VM data manager app 104 at datamanagement system 102.

VM data manager app 104 stores the data received from virtual systemmanager 112 in one or more virtual machine data indexes in VM datarepository 126, which is communicatively coupled to data managementsystem 102. VM data manager app 104 comprises instructions for thedisplay of graphical interfaces that may be presented to customers forthe monitoring of events occurring in virtual environment 152 or fortroubleshooting any problems affecting virtual environment 152. VM datamanager app 104 may cause the graphical interfaces to display at acustomer device, such as client device 152, Client device 152 may be anycomputing device including but not limited to a personal computer,laptop, mobile phone, mobile device, tablet computer, or a wearablecomputing device.

Storage data collection node 128 collects data relating to storageenvironment 148 from storage controller 130. In on embodiment, storagedata collection node 128 collects the data by making calls to an APImade available by storage controller 130. VM data collection node 110and storage data collection node 128 may be forwarders such asforwarders 701 in FIG. 7.

Storage controller 130 manages the storage of data across variousstorage units, such as storage units 140, 142, and 144. Storage units140, 142, and 144 may each be separate physical disks managed by storagecontroller 130. Storage controller 130 performs a variety of storagemanagement tasks, such as selecting the layout of data across differentstorage units and monitoring the performance of different storage units.

Virtual machines in virtual environment 152 may store their data instorage environment 148. Storage controller 130 may present portions ofdifferent storage units as a single contiguous volume to a virtualmachine. For example, virtual machine 118 may send a request to storagecontroller 130 to store or retrieve data from a particular volume, andstorage controller 130 may determine the locations at which to store thedata or from which to retrieve the data in response to the request. Thedetermined locations may span multiple storage units.

Storage data collection node 128 may be configured to re-structure orotherwise modify the data obtained from storage controller 130 toconform to a particular format before forwarding the data to storagedata manage app 106 at data management system 102. VM data manager app104 and storage data manager app 106 may both modify the data theyrespectively obtain to conform to the same format for easier retrievalof both types of data.

Storage data manager app 106 stores the data received from storagecontroller 130 in one or more storage data indexes in storage datarepository 150, which is communicatively coupled to data managementsystem 102. Storage data manager app 106 also comprises instructions forthe display of graphical interfaces that may be presented to customersfor monitoring events occurring in storage environment 148 or fortroubleshooting any problems affecting storage environment 148. Storagedata manager app 106 may cause the graphical interfaces to display at acustomer device, such as client device 152.

Search unit 108 in VM data manager app 104 may search for data in bothVM data repository 126 and storage data repository 150 and may perform acorrelation of the retrieved data. Thus, VM data manager app 104 mayhave access to both virtual machine data records stored in VM datarepository 126 and storage data records stored in storage datarepository 150. In other embodiments, storage data manager app 106 mayperform the searching and correlation and, in such an embodiment, searchunit 108 may be a component of storage data manager app 106.

In an embodiment, software used to implement virtual environment 152,including software, which when executed, performs the functionality ofvirtual system manager 112 and virtual machine managers 114 and 120 isprovided by vendor of virtualization software that is different than thestorage vendor that provides software used to implement storageenvironment 148. The storage vendor may provide software that performsthe functions of storage controller 130. For example, a virtualizationcompany such as VMware, Inc. may provide software that performs thefunctions of virtual system manager 112 and virtual machine managers 114and 120 and a storage company such as NetApp, Inc. or EMC Corp. mayprovide software that performs the functions of storage controller 130.

3.0. Example Collection, Correlation, and Display Processes

FIG. 2 illustrates an example process for the collection, correlation,and display of data obtained from a virtual system manager and a storagecontroller. In one embodiment, the process of FIG. 2 is performed at oneor more computers within data management system 102.

At block 210, VM data manager app 104 obtains, from virtual systemmanager 112, which manages one or more virtual machines, virtual machinedata records including one or more particular virtual machine datarecords relating to a particular virtual machine and identifying aparticular volume that is configured for use by the particular virtualmachine. The received virtual machine data records may relate to aplurality of virtual machines including the particular virtual machineto which the particular virtual machine data records relate.

The virtual machine data records may indicate a variety of informationincluding, but not limited to, the performance of virtual machines 116,118, 122, and 124 (e.g., CPU usage and/or memory usage by each of thevirtual machines, etc.), the performance of virtual machine managers 114and 120 which host virtual machines 116, 118, 122, and 124, the names ofvirtual machines 116, 118, 122, and 124 and virtual machine managers 114and 120, the topology of virtual environment 152 (e.g., for each virtualmachine, an identification of the physical machine that hosts thevirtual machine and, for each virtual machine, the virtual systemmanager that manages the virtual machine, etc.), and tasks and eventsthat occurred within virtual environment 152 (e.g., a log ofconfiguration changes to virtual machines 116, 118, 122, and 124, etc.).The virtual machine data records may identify a volume that a virtualmachine uses by specifying the name of the volume and identifying thestorage controller that manages the volume, such as by specifying thestorage controller's Internet Protocol (IP) address. In an embodiment,the virtual machines in virtual environment 152 use volumes in storageenvironment 148 for their data storage needs and the volumes in storageenvironment 148 are managed by a separate device and/or application thanvirtual system manager 112. Thus, virtual system manager 112 may notprovide performance information for any of the volumes used by thevirtual machines in virtual environment 152.

Data management system 102 may receive virtual machine data records fromvirtual system manager 112 via data collector node 110. In anembodiment, data collector node 110 obtains the data from virtual systemmanager 112 by periodically issuing requests for particular informationto virtual system manager 112. Data collector node 110 may modify dataobtained from virtual manager 114, such as reformatting the data toconform to a particular format, before forwarding the data to datamanagement system 102.

At block 220, VM data manager app 104 stores the virtual machine datarecords. The virtual machine data records may be stored within indexesat VM data repository 126. In some embodiments, data management system102 may modify the virtual machine data records to conform to aparticular format in addition to, or instead of, any modifications thatdata collector node 110 performs to the virtual machine data records.

At block 230, storage data manager app 106 obtains, from a storagecontroller, storage data records including one or more particularstorage data records that specify performance information associatedwith the particular volume. The received storage data records may relateto a plurality of volumes including the particular volume to which theparticular storage data records relate.

Storage data records may indicate a variety of information including,but not limited to, the performance of various storage entities instorage environment 148, such as storage controller 130, storage units140, 142, 144, volume 146, etc. Performance of the various storageentities may be indicated using any of a variety of metrics including anaverage or median amount of latency for requests sent to the storageentity or as an amount of input/output operations performed per secondby the storage entity. Storage data records may specify the names andcapacity of various storage entities in storage environment 148 (e.g.,storage units 140, 142, 144, and volume 146), the Internet Protocol (IP)addresses of storage controller 130, and the topology of the storageenvironment (e.g., an identification of which storage units and whichvolumes are managed by which storage controllers, etc.). The storagedata records may identify a volume by specifying a volume name andidentifying the storage controller that manages the volume.

In an embodiment, storage controller 130 is a separate device and/orapplication than virtual system manager 112 and storage controller 130only provides information about the storage environment 148 and does notprovide any general information about the performance of virtualmachines that utilize storage entities in storage environment 148. Forexample, although storage data records received from storage controller130 may indicate an amount of disk space utilized by a virtual machine,the storage data records may not specify how the virtual machine isperforming.

Storage data manager app 106 may receive storage data records fromstorage controller 130 via storage data collection node 128. In anembodiment, storage data collection node 128 obtains the data fromstorage controller 130 by periodically issuing requests for particularinformation to storage controller 130. Storage data collection node 128may modify data obtained from storage controller 130, such as byreformatting the data to conform to a particular data format, beforeforwarding the data to data management system 102.

At block 240, storage data manager app 106 stores the storage datarecords. The storage data records may be stored within indexes atstorage data repository 150. In some embodiments, data management system102 may modify the storage data records to conform to a particularformat in addition to, or instead of, any modifications that datacollector node 128 performs to the storage data records.

Although the storage data records and the virtual machine data recordsmay be obtained by different data collection nodes and/or stored bydifferent applications, VM data manager app 104 and data manager app 106may modify the virtual machine data records and storage data recordsrespectively to conform to the same data format. Such an approach mayallow for easier correlation of virtual machine data records and storagedata records.

At block 250, VM data manager app 104 determines, based on informationin the particular virtual machine data records and information in theparticular storage data records, that the particular storage datarecords relate to the particular volume used by the particular virtualmachine.

At block 260, VM data manager app 104 causes, in response to thedetermination, display of one or more graphical user interfacesdisplaying virtual machine information relating to the particularvirtual machine in association with volume performance informationrelating to the particular volume, where the virtual machine informationis determined based on the particular virtual machine data records andthe volume performance information is determined based on the particularstorage data records.

Example processes for the indexing, searching, and display of virtualmachine information is described in U.S. patent application Ser. No.14/167,316, titled “Correlation For User-Selected Time Ranges Of ValuesFor Performance Metrics Of Components In An Information-TechnologyEnvironment With Log Data From That Information-Technology Environment”filed Jan. 29, 2014, the entire contents of which are herebyincorporated by reference for all purposes as if set forth herein.

FIG. 3 illustrates an example process for the display of virtual machineinformation relating to a particular virtual machine in association withvolume performance information. In one embodiment, the process of FIG. 3is performed at VM data manager app 104.

At block 310, VM data manager app 104 causes display of a firstgraphical interface displaying information about a virtual machineincluding performance information relating to the virtual machine and anidentification of a volume associated with the virtual machine. Thefirst graphical interface may be displayed at display device 152.

The first graphical interface may indicate information determined basedon information received from virtual machine managers such as virtualmachine manager 114 and not based on information received from storagecontroller such as storage controller 130.

FIG. 4 illustrates an example graphical interface that includesperformance information relating to the virtual machine and anidentification of a volume associated with the virtual machine.

Interface 400 depicts an example virtual machine view which identifiesdifferent attributes of a particular virtual machine, including the nameof the virtual machine (item 404), the operating system (item 406),power state (408), and the status of tools available to the virtualmachine (item 410), the relationship between the amount of CPUs andcores available to the virtual machine (item 412), the amount of memoryavailable to the virtual machine (item 414), the cluster to which thevirtual machine belongs, where the cluster is a grouping of physicalhosts(item 416), and the physical host of the virtual machine (item418).

Interface 400 also identifies the name of the volume configured for usewith the particular virtual machine (item 420), the amount of disk spacecommitted for use by the particular virtual machine (item 422), theamount of disk uncommitted for use by the particular virtual machine(item 424), the amount of unshared disk space (item 426), whether thevolume is accessible to the particular virtual machine (item 428), thepath that the particular virtual machine uses to connect to the volume(item 430), and the uniform resource locator (URL) of the volume (item432).

Region 434 describes properties of recent changes to the configurationof the particular virtual machine, including the time at which theconfiguration change was performed (item 436), the description of theconfiguration change (item 438), the state of the configuration change(item 402), the type of task that caused the configuration change (item440), whether the task was scheduled (item 442), whether theconfiguration change was cancelled (item 444), and the system hostingthe particular virtual machine at the time of the configuration change(item 446).

Region 448 identifies information relating to any migrations that theparticular virtual machine may have experienced from one physical hostto another. For example, region 448 may identify, for each recentmigration, the physical host of the particular virtual machine beforethe migration, the physical host of the particular virtual machine aftermigration, and the time at which the migration occurred.

Region 450 identifies performance information for the particular virtualmachine. Graph 452 indicates the average CPU latency for all the CPUsused by the particular virtual machine at different times. The averageCPU latency is represented as a percentage of time the particularvirtual machine is in the ready state awaiting CPU resources from itsphysical host.

Graph 452 indicates average CPU latencies for the past four hours.According to various embodiments, a variety of performance statisticsmay be presented. For example, in response to a user selecting theresource of memory for which to view performance information using menu454, graph 452 may update to indicate performance related to memoryresources.

Using menu 456, a user may specify whether performance statistics shouldbe aggregated or be specific to a particular resource (i.e., aparticular CPU). Using menu 458, a user may select to view a differentperformance metric such as the amount of memory pages that were used bythe particular virtual machine at various times. Using menu 460, theuser may specify which types of metric values to view (e.g., average,maximum, or minimum values).

Referring to FIG. 3, at block 320, data management system 102 receives aselection to view volume information. A user may indicate a selection toview volume information by selecting item 420, which specifies the nameof the volume configured for use with the particular virtual machine.

At block 330, in response to receiving the selection, data managementsystem 102 identifies storage data records associated with the selectedvolume. For example, VM data manager app 104 may determine the volumeidentifier of the selected volume by searching virtual machine datarecords in VM data repository 126. In particular, search unit 108 maylocate the virtual machine data records associated with the particularvirtual machine for which interface 400 displays information and searchfor a volume identifier in the located virtual machine data records. Inresponse to determining the volume identifier of the selected volume,search unit 108 of VM data manager app 104 may search storage datarepository 150 for storage data records relating to the selected volumeby searching for storage data records that include the determined volumeidentifier.

In one embodiment, a volume may be identified in virtual machine datarecords and storage data records by a volume name and an IP address ofthe controller that manages the volume. In response to determining thevolume name and IP address of the selected volume based on the virtualmachine data records, VM data manager app 104 may cause storage datarecords containing the same determined volume name and IP address to beretrieved from storage data repository 150. Performance information forthe selected volume may be determined for display based on the retrievedstorage data records. For example, performance information fromdifferent performance information records may be aggregated anddisplayed in graph format.

Data management system 102 may comprise indexer(s) and search head(s)such as indexers 702 and search head 704 for the indexing and searchingof virtual machine data records and storage data records. In anembodiment, in response to receiving the selection, a search headsformulates a schema for retrieving storage data records associated withthe selected volume and distributes the schema to one or more indexers.The one or more indexers may apply the late-binding schema to eventsstored in one or more data repositories and return the retrieved storagedata records to the search head. The search head may determine theperformance information for the selected volume and other informationfor display based on the retrieved storage data records.

At block 340, VM data manager app 104 causes display of a secondgraphical interface displaying information about the volume includingperformance information relating to the volume. VM data manager app 104may determine and send the instructions for display of the secondgraphical interface to display device 152. VM data manager app 104 maybe located on the search head that receives the retrieved data fromdifferent indexers.

The second graphical interface may include information determined basedon both virtual machine data records and storage data records. Thevirtual machine data records and storage data records may be storedaccording to the same format. For example, any identifications of avolume name in both the virtual machine data records and storage datarecords may be tagged with the same field name. As a result, VM datamanager app 104 may determine that the value for a particular field incertain virtual machine data records and retrieve the records fromstorage data repository 150 that contain the same value for theparticular field.

FIGS. 5A and 5B illustrate an example graphical interface that includesinformation about a particular volume. FIG. 5A illustrates one portionof the example graphical interface and FIG. 5B illustrates a secondportion of the same example graphical interface. Interface 500 is anexample general volume view that displays information relating to theselected volume.

In region 520, interface 500 identifies the amount of space available inthe particular volume (item 502), the total space in the particularvolume (item 504), the amount of space provisioned in the particularvolume for virtual machines (item 506), and the percentage of the volumethat is overprovisioned (item 508).

Region 554 identifies the path of the particular volume (item 556), theURL of the particular volume (item 558), and the number of virtualmachines that utilize the particular volume (item 560).

Region 510 identifies, for each virtual machine using the particularvolume, the name of the physical host of the virtual machine (item 512),the name of the virtual machine (item 514), the amount of spacecommitted to the virtual machine (item 516), the amount of uncommittedspace in the particular volume for the virtual machine (item 518), andthe amount of space provisioned for the virtual machine (item 520).

Interface 500 displays various graphs indicating the performance of theparticular volume and the storage controller that manages the particularvolume. For example, graph 522 indicates the latency rate for thestorage controller that manages the particular volume over the past fourhours in milliseconds. Item 524 identifies the name of the storagecontroller that manages the particular volume. Line 526 indicates theaverage latency for write operations, line 528 indicates the averagelatency for read operations, and line 530 indicates the average latencyfor other operations.

In FIG. 5B, graph 532 indicates the average input/output operationsperformed per second (IOPS) by the storage controller that manage theparticular volume over the past four hours. Line 534 indicates theaverage IOPS for write operations, line 536 indicates the average IOPSfor read operations, and line 538 indicates the average IOPS for alloperations.

Graph 540 indicates the latency rate for the particular volume over thepast four hours in milliseconds. Line 542 indicates the average latencyfor write operations, line 544 indicates the average latency for readoperations, and line 546 indicates the average latency for alloperations.

Graph 546 indicates the average input/output operations performed persecond (IOPS) by the storage controller that manage the particularvolume over the past four hours. Line 548 indicates the average IOPS forwrite operations, line 550 indicates the average IOPS for readoperations, line 552 indicates the average IOPS for other operations,and line 554 indicates the average IOPS for all operations.

In some embodiments, the information displayed in region 520 isdetermined based on virtual machine data records obtained from virtualmachine manager 114 and the information displayed in regions 554 and 510and graphs 522, 532, 540, and 546 is determined based on storage datarecords received from storage controller 130.

FIGS. 4, 5A, and 5B illustrate merely one example embodiment in whichvirtual machine information from virtual machine manager 114 may bedisplayed in association with storage data from storage controller 130.In other embodiments, virtual machine information from virtual machinemanager 114 may be displayed in association with storage data fromstorage controller 130 in alternate ways. For example, in otherembodiments, graphs indicating the performance of both a virtual machineand the storage entities associated with the virtual machine may bedisplayed within the same graphical interface.

Although the process of FIG. 3 is described as having been performed atVM data manager app 104, in other embodiments, the process of FIG. 3 maybe performed at storage data manager app 106 or some other application.For example, storage data manager app 104 may cause display of graphicalinterfaces depicting both virtual machine information and storageinformation determined based on virtual machine data records and storagedata records.

In response to a user selection to view further information aboutstorage resources, further information about storage controller 130 maybe displayed in a third graphical interface. The user may select item524 in interface 500, which identifies the name of a storage controller130. The third graphical interface, a detailed storage view, may bedisplayed in and by a separate application, such as storage data managerapplication 106. The third graphical interface may indicate performancemetrics such as how the CPU of storage controller 130 is performing andhow many input/output operations storage controller 130 is handling persecond (IOPS).

The second and/or third graphical interface may also identify thephysical disks that a particular volume spans, VM data manager app 104or storage data manager app 106 app may determine which disks a volumespans based on storage data records in storage data repository 150.

Using the approaches described herein, a user may troubleshootperformance issues in a virtual machine more efficiently and easily thanbefore. As one example, after noticing that a virtual machine isunder-performing, a user may navigate to a virtual machine view (e.g.,interface 400) to determine the status of the virtual machine. Based onthe information displayed in the virtual machine view, the user maydetermine whether the performance issue is being caused by a resource ofthe physical host. For example, if none of the information in interface400 indicates an existence of an issue with the resources of thephysical host, the user may choose to view volume information byselecting the volume that contains the virtual machine's data (e.g.,item 420 in interface 400). If the information in the volume view (e.g.,interface 500) indicates poor performance or a sharp change in theperformance of a volume or a storage controller, the user may determinethat the virtual machine performance is indeed being affected by aproblem in the storage environment. If so, a user may view furtherinformation about the storage environment, for example by selecting item524, which identifies the name of the storage controller that managesthe volume. Selecting item 524 may result in the display of a thirdinterface, a detailed storage view, that provides details such as whichdisks a volume spans and what the performance statistics are for thestorage controller that manages a particular volume (e.g., CPUutilization metrics). Based on the performance metrics displayed in thethird interface, the user may determine which physical components may becausing the issue. For example, based on the second graphical interface,the user may determine whether it is a problem in the storageenvironment or elsewhere and, if it is a problem in the physicalenvironment, based on the third graphical interface, the user maydetermine whether the problem is being caused by a particular disk, aparticular storage controller, or some other storage entity.

According to various embodiments, one or more of the steps of theprocesses illustrated in FIGS. 2 and 3 may be removed or the ordering ofthe steps may be changed. Additionally, although separate embodimentsare discussed herein, any combination of embodiments and/or partialembodiments discussed herein may be combined to form furtherembodiments.

In an embodiment, system 100 may be a distributed system where VM datacollection node 110, data management system 112, storage data collectionnode 128, VM data repository 126, and storage data repository 150 eachrepresent multiple entities in a distributed system. For example, VMdata collection node 110 and storage data collection node 128 may eachrepresent multiple data collection nodes that forward information fromdifferent virtual machine managers and different storage controllers,respectively, to different computers within data management system 102.Virtual machine data repository 126 and storage data repository 150 mayeach represent multiple, different repositories within which virtualmachine data and storage data is stored. VM data manager app 104 andstorage data manager app 106 may each execute on multiple machines anddifferent instances of the apps may store information in differentrepositories.

In response to a request for information, such as a request to viewinformation for a volume, records of all the different repositories thatcollectively represent virtual machine data repository 126 and storagedata repository 150 may be searched. In some cases, they repositoriesmay be searched by different search units on different machines. Recordsfrom the different repositories that collectively represent virtualmachine data repository 126 and storage data repository 150 may be usedto determine performance information for display in a single graphicalinterface.

Additionally, in other embodiments other performance metrics of virtualenvironment 152 and storage environment 148 may be identified in thegraphical interface(s) or used to determine performance information thatis identified in the graphical interface(s).

Metrics relating to virtual environment 152 may describe properties ofthe virtual environment, a particular virtual machine, a particularphysical host, a particular virtual machine manage, and/or a particularvirtual system manager. Performance metrics may include a CPUperformance metric, a memory performance metric, a summary performancemetric, a performance metric based on a max CPU usage, a performancemetric based on a max memory usage, a performance metric based on aballooned memory, a performance metric based on a swapped memory, aperformance metric based on an average memory usage percentage, aperformance metric based on the total amount of memory that is reclaimedfrom all of the VMs on a host, a performance metric based on the totalamount of memory that is being swapped from all of the VMs on a host, aperformance metric that changes state based on the remaining disk spaceon a data store, a performance metric that changes state based on howmuch space is over-provisioned (i.e., negative numbers are arepresentation of an under-provisioned data store), a performance metricbased on a VM's average CPU usage in percent, a performance metric basedon a VM's average memory usage in percent, a performance metric based ona VM's state waiting for CPU time, a performance metric based on a VM'smemory that is actively in use, a performance metric based on a VM'smemory saved by memory sharing, a performance metric based on a VM'smemory used to power the VM, a performance metric based on physicalmemory that is mapped to a VM (i.e., memory not including overheadmemory), a performance metric based on an amount of physical memory thatis being reclaimed by a host through a ballooning driver, a performancemetric based on memory that is being read by a VM from a host's swapfile, a performance metric based on an amount of memory a VM has had towrite to a swap file, a performance metric based on an amount of memoryfrom a VM that has been swapped by a host. Other example metrics mayinclude task assignment count, task assignment types, task completioncounts, and/or may describe migrations to/from a virtual machine orto/from a host.

Included below is a non-exhaustive list of known virtual machineperformance metrics relating to virtual environment 152 that may beidentified in graphical interface(s) displayed by data management 102 orused to determine performance information that is identified in thegraphical interface(s).

PercentHighCPUVm, PercentHighMemVm, PercentHighSumRdyVm, VMInvCpuMaxUsg,VMInvMemMaxUsg, PercentHighBalloonHosts, PercentHighSwapHosts,PercentHighCPUHosts, BalloonedMemory_MB, swappedMemory_MB,RemainingCapacity_GB, Overprovisioned_GB, p_average_cpu_usage_percent,p_average_mem_usage_percent, p_summation_cpu_ready_millisecond,p_average_mem_active_kiloBytes, p_average_mem_consumed_kiloBytes,p_average_mem_overhead_kiloBytes, p_average_mem_granted_kiloBytes,p_average_mem_vmmemctl_kiloBytes, 20 p_average_mem_swapin_kiloBytes,p_average_mem_swapout_kiloBytes, p_average_mem_swapped_kiloBytes,p_average_disk_read_kiloBytesPerSecond,p_average_disk_write_kiloBytesPerSecond,p_average_disk_usage_kiloBytesPerSecond,p_summation_disk_numberWrite_number, p_summation_disk_numberRead_number,p_latest_disk_maxTotalLatency_millisecond,p_summation_disk_commandsAborted_number,p_summation_disk_busResets_number,p_average_net_received_kiloBytesPerSecond,p_average_net_transmitted_kiloBytesPerSecond,p_average_net_usage_kiloBytesPerSecond, p_average_cpu_usage_percent,p_summation_cpu_ready_millisecond, p_average_mem_usage_percent,p_average_mem_active_kiloBytes, p_average_mem_consumed_kiloBytes,p_average_mem_overhead_kiloBytes, p_average_mem_granted_kiloBytes,p_average_mem_vmmemctl_kiloBytes, p_average_mem_swapin_kiloBytes,p_average_mem_swapout_kiloBytes, p_average_mem_∥SwapUsed_kiloBytes,p_average_disk_numberReadAveraged_number,p_average_disk_numberWriteAveraged_number,p_average_disk_usage_kiloBytesPerSecond,p_summation_disknumberWrite_number, summation_disk_numberRead_number,p_latest_diskmaxTotalLatency_millisecond,p_average_disk_queueLatency_millisecond,p_summation_disk_commandsAborted_number, p_summation_d 5isk_busResets_number, p_average_net_received_kiloBytesPerSecond,p_average_net_transmitted_kiloBytesPerSecond,p_average_net_usage_kiloBytesPerSecond, p_average_cpu_demand_megaHertz,p_average_cpu_demand_megaHertz, p_average_cpu_usagemhz_megaHertz,p_average_cpu_usagemhz_megaHertz and/or AvgUsg_pctPercentHighCPUVm,PercentHighMemVm, PercentHighSumRdyVm, VMInvCpuMaxUsg, VMInvMemMaxUsg,PercentHighBalloonHosts, PercentHighSwapHosts, PercentHighCPUHosts,BalloonedMemory_MB, swappedMemory_MB, RemainingCapacity_GB,Overprovisioned_GB, p_average_cpu_usage_percent,p_average_mem_usage_percent, p_summation_cpu_ready_millisecond,p_average_mem_active_kiloBytes, p_average_mem_consumed_kiloBytes,p_average_mem_overhead_kiloBytes, p_average_mem_granted_kiloBytes,p_average_mem_vmmemctl_kiloBytes, p_average_mem_swapin_kiloBytes,p_average_mem_swapout_kiloBytes, p_average_mem_swapped_kiloBytes,p_average_disk_read_kiloBytesPerSecond,p_average_disk_write_kiloBytesPerSecond,p_average_disk_usage_kiloBytesPerSecond,p_summation_disk_numberWrite_number, p_summation_disk_numberRead_number,p_latest_disk_maxTotalLatency_millisecond,p_summation_disk_commandsAborted_number,p_summation_disk_busResets_number,p_average_net_received_kiloBytesPerSecond,p_average_net_transmitted_kiloBytesPerSecond,p_average_net_usage_kiloBytesPerSecond, p_average_cpu_usage_percent,p_summation_cpu_ready_millisecond, p_average_mem_usage_percent,p_average_mem_active_kiloBytes, p_average_mem_consumed_kiloBytes,p_average_mem_overhead_kiloBytes, p_average_mem_granted_kiloBytes,p_average_mem_vmmemctl_kiloBytes, p_average_mem_swapin_kiloBytes,p_average_mem_swapout_kiloBytes, p_average_mem_∥SwapUsed_kiloBytes,p_average_disk_numberReadAveraged_number,p_average_disk_numberWriteAveraged_number,p_average_disk_usage_kiloBytesPerSecond,p_summation_disk_numberWrite_number, p_summation_disk_numberRead_number,p_latest_disk_maxTotalLatency_millisecond,p_average_disk_queueLatency_millisecond,p_summation_disk_commandsAborted_number,p_summation_disk_busResets_number,p_average_net_received_kiloBytesPerSecond,p_average_net_transmitted_kiloBytesPerSecond,p_average_net_usage_kiloBytesPerSecond, p_average_cpu_demand_megaHertz,p_average_cpu_demand_5 megaHertz, p_average_cpu_usagemhz_megaHertz,p_average_cpu_usagemhz_megaHertz and/or AvgUsg_pct.

Of course any of the above or below listed performance metrics couldalso or alternatively be monitored and reported in any of: bytes,MegaBytes, GigaBytes and/or any other byte or memory amount.

Any performance metrics described herein could also or alternatively bemonitored and reported in any of: hertz, MegaHertz, GigaHertz and/or anyhertz amount. Moreover, any of the performance metrics disclosed hereinmay be monitored and reported in any of percentage, relative, and/orabsolute values.

Other performance metrics that may be collected or displayed may includeany type of cluster performance metrics, such as:latest_clusterServices_cpufairness_number,average_clusterServices_effectivecpu_megaHertz,average_clusterServices_effectivemem_megaBytes,latest_clusterServices_failover_number and/orlatest_clusterServices_memfairness_number.

CPU performance metrics that may be collected or displayed may includeany of: average_cpu_capacity.contention_percent,average_cpu_capacity.demand_megaHertz,average_cpu_capacity.entitlement_megaHertz,average_cpu_capacity.provisioned_megaHertz,average_cpu_capacity.usage_megaHertz, none_cpu_coreUtilization_percent,average_cpu_coreUtilization_percent,maximum_cpu_coreUtilization_percent,minimum_cpu_coreUtilization_percent,average_cpu_corecount.contention_percent,average_cpu_corecount.provisioned_number,average_cpu_corecount.usage_number, summation_cpu_costop_millisecond,latest_cpu_cpuentitlement_megaHertz, average_cpu_demand_megaHertz,latest_cpu_entitlement_megaHertz, summation_cpu_idle_millisecond,average_cpu_latency_percent, summation_cpu_maxlimited_millisecond,summation_cpu_overlap_millisecond, summation_cpu_ready_millisecond,average_cpu_reservedCapacity_megaHertz, summation_cpu_run_millisecond,summation_cpu_swapwait_millisecond, summation_cpu_system_millisecond,average_cpu_totalCapacity_megaHertz, average_cpu_totalmhz_megaHertz,none_cpu_us 5 age_percent, average_cpu_usage_percentminimum_cpu_usage_percent, maximum_cpu_usage_percent,none_cpu_usagemhz_megaHertz, average_cpu_usagemhz_megaHertz,minimum_cpu_usagemhz_megaHertz, maximum_cpu_usagemhz_megaHertz,summation_cpu_used_millisecond, none_cpu_utilization_percent,average_cpu_utilization_percent, maximum_cpu_utilization_percent,minimum_cpu_utilization_percent and/or summation_cpu_wait_millisecond.

Host-based replication (“hbr”) performance metrics that may be collectedor displayed may include any of:average_hbr_hbrNetRx_kiloBytesPerSecond,average_hbr_hbrNetTx_kiloBytesPerSecond and/oraverage_hbr_hbrNumVms_number,

Management Agent performance metrics that may be collected or displayedmay include any of: average_managementAgent_5 cpuUsage_megaHertz,average_managementAgent_memUsed_kiloBytes,average_managementAgent_swapIn_kiloBytesPerSecond,average_managementAgent_swapOut_kiloBytesPerSecond and/oraverage_managementAgent_swapUsed_kiloBytes.

Memory performance metrics that may be collected or displayed mayinclude any of:

none_mem_active_kiloBytes, average_mem_active_kiloBytes,minimum_mem_active_kiloBytes, maximum_mem_active_kiloBytes,average_mem_activewrite_kiloBytes,average_mem_capacity.contention_percent,average_mem_capacity.entitlement_kiloBytes,average_mem_capacity.provisioned_kiloBytes,average_mem_capacity.usable_kiloBytes,average_mem_capacity.usage_kiloBytes,average_mem_capacity.usage,userworld_kiloBytes,average_mem_capacity.usage.vm_kiloBytes,average_mem_capacity.usage.vmOvrhd_kiloBytes,average_mem_capacity.usage.vmkOvrhd_kiloBytes,average_mem_compressed_kiloBytes,average_mem_compressionRate_kiloBytesPerSecond,none_mem_consumed_kiloBytes, average_mem_consumed_kiloBytes,minimum_mem_consumed_kiloBytes, maximum_mem_consumed_kiloBytes,average_mem_consumed.userworlds_kiloBytes,average_mem_consumed.vms_kiloBytes,average_mem_decompressionRate_kiloBytesPerSecond,average_mem_entitlement_kiloBytes, none_mem_granted_kiloBytes,average_mem_granted_kiloBytes, minimum_mem_granted_kiloBytes,maximum_mem_granted_kiloBytes, none_mem_heap_kiloBytes,average_mem_heap_kiloBytes, minimum_mem_heap_kiloBytes,maximum_mem_heap_kiloBytes, none_mem_heapfree_kiloBytes,average_mem_heapfree_kiloBytes, minimum_mem_heapfree_kiloBytes,maximum_mem_heapfree_kiloBytes, average_mem_latency_percent,none_mem_∥SwapIn_kiloBytes, average_mem_∥SwapIn_kiloBytes,maximum_mem_∥SwapIn_kiloBytes, minimum_mem_∥SwapIn_kiloBytes,average_mem_∥SwapInRate_kiloBytesPerSecond, none_mem_∥SwapOut_kiloBytes,average_mem_∥SwapOut_kiloBytes, maximum mem_∥SwapOut_kiloBytes,minimum_mem_∥SwapOut_kiloBytes,average_mem_∥SwapOutRate_kiloBytesPerSecond,none_mem_∥SwapUsed_kiloBytes, average_mem_∥SwapUsed_kiloBytes,maximum_mem_∥SwapUsed_kiloBytes, minimum_mem_∥SwapUsed_kiloBytes,average_mem_lowfreethreshold_kiloBytes,latest_mem_mementitlement_megaBytes, none_mem_overhead_kiloBytes,average_mem_overhead_kiloBytes, minimum_mem_overhead_kiloBytes,maximum_mem_overhead_kiloBytes, average mem_overheadMax_kiloBytes,average_mem_overheadTouched_kiloBytes,average_mem_reservedCapacity_megaBytes,average_mem_reservedCapacity,userworld_kiloBytes,average_mem_reservedCapacity.vm_kiloBytes,average_mem_reservedCapacity.vmOvhd_kiloBytes,average_mem_reservedCapacity.vmkOvrhd_kiloBytes,average_mem_reservedCapacityPct_percent, none_mem_shared_kiloBytes,average_mem_shared_kiloBytes, minimum_mem_shared_kiloBytes,maximum_mem_shared_kiloBytes, none_mem_sharedcommon_kiloBytes,average_mem_sharedcommon_kiloBytes, minimum_mem_sharedcommon_kiloBytes,maximum_mem_sharedcommon_kiloBytes, latest_mem_state_number,none_mem_swapin_kiloBytes, average_mem_swapin_kiloBytes,minimum_mem_swapIn_kiloBytes, maximum_mem_swapin_kiloBytes,none_mem_swapOut_kiloBytes, average_mem_swapOut_kiloBytes,minimum_mem_swapOut_kiloBytes, maximum_mem_swapOut_kiloBytes, nonemem_swapin_kiloBytes, average_mem_swapin_kiloBytes,maximum_mem_swapin_kiloBytes, minimum_mem_swapin_kiloBytes,average_mem_swapinRate_kiloBytesPerSecond, none_mem_swapout_kiloBytes,average_mem_swapout_kiloBytes, maximum_mem_swapout_kiloBytes,minimum_mem_swapout_kiloBytes,average_mem_swapoutRate_kiloBytesPerSecond, none_mem_swapped_kiloBytes,average_mem_swapped_kiloBytes, minimum_mem_swapped_kiloBytes,maximum_mem_swapped_kiloBytes, none_mem_swaptarget_kiloBytes,average_mem_swaptarget_kiloBytes, minimum_mem_swaptarget_kiloBytes,maximum_mem_swaptarget_kiloBytes, none_mem_swapunreserved_kiloBytes,average_mem_swapunreserved_kiloBytes,minimum_mem_swapunreserved_kiloBytes, maximum_mem_swapunreserved-5kiloBytes, none_mem_swapused_kiloBytes, average_mem_swapused_kiloBytes,minimum_mem_swapused_kiloBytes, maximum_mem_swapused_kiloBytes,none_mem_sysUsage_kiloBytes, average_mem_sysUsage_kiloBytes,maximum_mem_sysUsage_kiloBytes, minimum_mem_sysUsage_kiloBytes,average_mem_totalCapacity_megaBytes, average_mem_totalmb_megaBytes,none_mem_unreserved_kiloBytes, average_mem_unreserved_kiloBytes,minimum_mem_unreserved_kiloBytes, maximum_mem_unreserved_kiloBytes,none_mem_usage_percent, average_mem_usage_percent,minimum_mem_usage_percent, maximum_mem_usage_percent,none_mem_vmmemctl_kiloBytes, average_mem_vmmemctl_kiloBytes,minimum_mem_vmmemctl_kiloBytes, maximum_mem_vmmemctl_kiloBytes,none_mem_vmmemctltarget_kiloBytes, average_mem_vmmemctltarget_kiloBytes,minimum_mem_vmmemctltarget_kiloBytes,maximum_mem_vmmemctltarget_kiloBytes, none_mem_zero_kiloBytes,average_mem_zero_kiloBytes, minimum_mem_zero_kiloBytes,maximum_mem_zero_kiloBytes, latest_mem_zipSaved_kiloBytes and/orlatest_mem_zipped_kiloBytes.

Network performance metrics that may be collected or displayed mayinclude any of: summation_net_broadcastRx_number,summation_net_broadcastTx_number,average_net_bytesRx_kiloBytesPerSecond,average_net_bytesTx_kiloBytesPerSecond, summation_net_droppedRx_number,summation_net_droppedTx_number, summation_net_errorsRx_number,summation_net_errorsTx_number, summation_net_multicastRx_number,summation_net_multicastTx_number, summation_net_packetsRx_number,summation_net_packetsTx_number, average_net_received_kiloBytesPerSecond,summation_net_throughput.contention_number,average_net_throughput.packetsPerSec_number,average_net_throughput.provisioned_kiloBytesPerSecond,average_net_throughput.usable_kiloBytesPerSecond,average_net_throughput.usage_kiloBytesPerSecond,average_net_throughput.usage.ft_kiloBytesPerSecond,average_net_throughput.usage, hbr_kiloBytesPerSecond,average_net_throughput.usage.iscsi_kiloBytesPerSecond,average_net_throughput.usage.nfs_kiloBytesPerSecond,average_net_throughput.usage.vm_kiloBytesPerSecond,average_net_throughput.usage.vmotion_kiloBytesPerSecond,average_net_transmitted_kiloBytesPerSecond,summation_net_unknownProtos_number, none_net_usage_kiloBytesPerSecond,average_net_usage_kiloBytesPerSecond,minimum_net_usage_kiloBytesPerSecond and/ormaximum_net_usage_kiloBytesPerSecond.

Power performance metrics that may be collected or displayed may includeany of: average_powercapacity.usable_watt,average_power_capacity.usage_watt,average_power_capacity.usagePct_percent, summation_power_energy_joule,average_power_power_watt and/or average_power_powerCap_watt.

Rescpu performance metrics that may be collected or displayed mayinclude any of: latest_rescpu_actav1_percent,latest_rescpu_actav15_percent, latest_rescpu_actav5_percent,latest_rescpu_actpk1_percent, latest_rescpu_actpk15_percent,latest_rescpu_actpk5_percent, latest_rescpu_maxLimited1_percent,latest_rescpu_maxLimited15_percent, latest_rescpu_maxLimited5_percent,latest_rescpu_runav1_percent, latest_rescpu_runav15_percent,latest_rescpu_runav5_percent, latest_rescpu_runpk1_percent, 25latest_rescpu_runpk15_percent, latest_rescpu_runpk5_percent,latest_rescpu_sample, count_number and/orlatest_rescpu_samplePeriod_millisecond.

System performance metrics that may be collected or displayed mayinclude any of: latest_sys_diskUsage_percent,summation_sys_heartbeat_number, latest_sys_osUptime_second,latest_sys_resourceCpuAct1_percent, latest_sys_resourceCpuAct5_percent,latest_sys_resourceCpuAllocMax_megaHertz,latest_sys_resourceCpuAllocMin_megaHertz,latest_sys_resourceCpuAllocShares_number,latest_sys_resourceCpuMaxLimited1_percent,latest_sys_resourceCpuMaxLimited5_percent,latest_sys_resourceCpuRun1_percent, latest_sys_resourceCpuRun5_percent,none_sys_resourceCpuUsage_megaHertz,average_sys_resourceCpuUsage_megaHertz, maximum-5sys_resourceCpuUsage_megaHertz, minimum_sys_resourceCpuUsage_megaHertz,latest_sys_resourceMemAllocMax_kiloBytes,latest_sys_resourceMemAllocMin_kiloBytes,latest_sys_resourceMemAllocShares_number,latest_sys_resourceMemConsumed_kiloBytes,latest_sys_resourceMemCow_kiloBytes,latest_sys_resourceMemMapped_kiloBytes,latest_sys_resourceMemOverhead_kiloBytes,latest_sys_resourceMemShared_kiloBytes,latest_sys_resourceMemSwapped_kiloBytes,latest_sys_resourceMemTouched_kiloBytes,latest_sys_resourceMemZero_kiloBytes and/or latest_sys_uptime_second.

Debug performance metrics that may be collected or displayed may includeany of: maximum_vcDebugInfo_activationlatencystats_millisecond,minimum_vcDebugInfo_activationlatencystats_millisecond,summation_vcDebugInfo_activationlatencystats_millisecond,maximum_vcDebugInfo_activationstats_number,minimum_vcDebugInfo_activationstats_number,summation_vcDebugInfo_activationstats_number,maximum_vcDebugInfo_hostsynclatencystats_millisecond,minimum_vcDebugInfo_hostsynclatencystats_millisecond,summation_vcDebugInfo_hostsynclatencystats_millisecond,maximum_vcDebugInfo_hostsyncstats_number,minimum_vcDebugInfo_hostsyncstats_number,summation_vcDebugInfo_hostsyncstats_number,maximum_vcDebugInfo_inventorystats_number,minimum_vcDebugInfo_inventorystats_number,summation_vcDebugInfo_inventorystats_number,maximum_vcDebugInfo_lockstats_number,minimum_vcDebugInfo_lockstats_number,summation_vcDebugInfo_lockstats_number,maximum_vcDebugInfo_lrostats_number,minimum_vcDebugInfo_lrostats_number,summation_vcDebugInfo_lrostats_number,maximum_vcDebugInfo_miscstats_number,minimum_vcDebugInfo_miscstats_number,summation_vcDebugInfo_miscstats_number,maximurm_vcDebugInfo_morefregstats_number,minimum_vcDebugInfo_morefregstats_number,summation_vcDebugInfo_morefregstats_number,maximum_vcDebugInfo_scoreboard_number,minimum_vcDebugInfo_scoreboard_number, summation_vcDebugInfo-5scoreboard_number, maximum_vcDebugInfo_sessionstats_number,minimum_vcDebugInfo_sessionstats_number,summation_vcDebugInfo_sessionstats_number,maximum_vcDebugInfo_systemstats_number,minimum_vcDebugInfo_systemstats_number,summation_vcDebugInfo_systemstats_number,maximum_vcDebugInfo_vcservicestats_number,minimum_vcDebugInfo_vcservicestats_number and/orsummation_vcDebugInfo_vcservicestats_number.

Resource performance metrics that may be collected or displayed mayinclude any of: average_vcResources_cpuqueuelength_number,average_vcResources_ctxswitchesrate_number,average_vcResources_diskqueuelength_number,average_vcResources_diskreadbytesrate_number,average_vcResources_diskreadsrate_number,average_vcResources_diskwritebytesrate_number,average_vcResources_diskwritesrate_number,average_vcResources_netqueuelength_number,average_vcResources_packetrate_number,average_vcResources_packetrecvrate_number,average_vcResources_packetsentrate_number,average_vcResources_pagefaultrate_number,average_vcResources_physicalmemusage_kiloBytes,average_vcResources_poolnonpagedbytes_kiloBytes,average_vcResources_poolpagedbytes_kiloBytes,average_vcResources_priviledgedcpuusage_percent,average_vcResources_processcpuusage_percent,average_vcResources_processhandles_number,average_vcResources_processthreads_number,average_vcResources_syscallsrate_number,average_vcResources_systemcpuusage_percent,average_vcResources_systemnetusage_percent,average_vcResources_systemthreads_number,average_vcResources_usercpuusage_percent and/oraverage_vcResources_virtualmemusage_kiloBytes.

VM operation performance metrics that may be collected or displayed mayinclude any of: latest_vmop_numChangeDS_number,latest_vmop_numChangeHost_number, latest_vmop_numChangeHostDS_number,latest_vmop_numClone_number, latest_vmop_numCreate_number,latest_vmop_numDeploy_number, latest_vmop_numDestroy_number,latest_vmop_numPoweroff_number, latest_vmop_numPoweron_number,latest_vmop_numRebootGuest_number, latest_vmop_numReconfigure_number,latest_vmop_numRegister_number, latest_vmop_numReset_number,latest_vmop_numSVMotion_number, latest_vmop_numShutdownGuest_number,latest_vmop_numStandbyGuest_number, latest_vmop_numSuspend_number,latest_vmop_numUnregister_number and/or latest_vmop_numVMotion_number.

Included below is a non-exhaustive list of known performance metricsrelating to storage environment 148 that may be identified in thegraphical interface(s) displayed by data management system 102 or usedto determine performance information that is identified in the graphicalinterface(s).

VOLUME PERF METRICS: other_ops_rate, other_latency_average,avg_latency_average, write_latency_average, read_ops_rate,write_ops_rate, read_latency_average, total_ops_rate, cifs_write_ops,wvblk_past_eof, cifs_read_latency, cifs_read_ops, clone_blks_copied,nfs_write_ops_rate, process_name,repl_metafile_logical_xferdw_indirect_blks_cnt, clone_read_redirected,clone_num_share_stopped, wvblk_rsrv_parent_overwrite_always,nfs_protocol_write_latency_labels, delete_log_labels,nfs_protocol_read_latency_delta, san_other_ops,bad_zombie_ind_blk_read_errnot_propagated, write_data_rate,wvsblk_vvrd_spcflags, iscsi_read_ops_rate,fcp_protocol_read_latency_labels, df_worker_rate, wvblk_snap_reserve,bad_container_user_blk_read_error_propagated, san_read_latency_average,clone_afs_sub_file, msgs_allowed_in_nvfailed_state,wv_info_blks_vbn_zero_in_plane0, iscsi_protocol_read_latency,wv_playlist_no_raidbufs, iscsi_protocol_write_latency_labels,nfs_read_latency_average, iscsi_write_data_rate, clone_snap_full_file,other_latency, cifs_otherlatency_average, fcp_protocol_other_latency,wvbd_whole_frees_o,flexcache_send_data, wv_playlist_entries,clone_inline_split_source_destination_dirty, write_data,wvblk_reclaim_time_done, nfs_write_data, wv_fsinfo_blks_used,wvblk_saved_fsinfo_private_inos_total,total_protocol_other_latency_delta, wv_fsinfo_fs_version,sub_clone_latencies_hist, nfs_read_latency, asynchronous_frees,iscsi_read_latency, clone_split_ra,repl_metafile_logical_xfer_buffer_blks_cnt,clone_inline_split_beyond_eof, wvsblk_lev0_over_nominal,wv_playlist_not_present, wvbd_active_frees, wv_fsinfo_blks_reserve,cifs_protocol_other_latency_labels, cad_iron_fixed,bad_fixable_blk_read_error_not_propagated,wv_playlist_apfi_collision_accesses, fcp_write_ops,bad_container_fixable_snap_blk_read_error_propagated,iscsi_protocol_read_latency_delta, wv_vol_type, clone_sizes_hist_labels,wvzmb_num_zmsgs_inuse, wvblk_rsrv_holes_cifs64,total_protocol_write_latency, sub_clone_latencies_hist_labels,flexcache_receive_data_rate, nfs_other_latency, cifs_read_data_rate,nfs_protocol_other_latency_labels, wv_playlist_prefetch_end_time,nfs_read_ops_rate, total_protocol_write_latency_labels,wvblk_rsrv_parent_holes, cifs_read_ops_rate,wv_playlist_prefetch_not_started, wv_fsinfo_blks_used_by_plane0,internal_msgs_rate, wv_playlist_load_end_time, read_ops,wv_fsinfo_blkr_cp, wv_fsinfo_blks64_blks_rsrv_holes_cifs,clone_inline_split_source_spec_vbn, wvblk_rsrv_overwrite,wv_playlist_misses,bad_container_fixable_snap_blk_read_error_not_propagated,nfs_protocol_read_latency_labels, clone_lookups, node_name,total_protocol_read_latency_labels, wv_fsinfo_blks_blks_rsrv_overwrite,wv_playlist_cont_indirects, wvi2p_wip_wi_size, wvdf enabled,iscsi_other_latency, bad_fixable_metafile_blk_read_error_not_propagated,wvblk_reclaim_time_reset, san_write_data, cifs_write_latency,clone_prune_tmpclonedir_err, delete_log,wvsblk_vvrd_spc_clone_inherited, nfs_protocol_other_latency_delta,write_ops, wvblk_saved_fsinfo_public_inos_reserve,wv_fsinfo_blks_vvol_dbys_df_cnt, write_blocks_rate,wv_fsinfo_blks_total, wvbd_owner_changed_y, cifs_protocol_read_latency,flexcache_other_ops_rate, fcp_other_ops,fcp_protocol_other_latency_delta, wvip_vvol_container_wi_blk_cnt,wv_playlist_prefetch_start_time, iscsi_read_data, extent_size,instance_name, iscsi_write_latency_average,wv_fsinfo_containment_version_vmalign,bad_container_user_blk_read_error_not_propagated, iscsi_write_data,nfs_read_ops, parent_aggr, san_read_ops, cad_clone_create_inserts,cifs_protocol_write_latency_delta, wvblk_rsrv_parent_overwrite,iscsi_read_ops, wv_fsinfo_public_inos_total, iscsi_write_ops_rate,iscsi_read_data_rate,bad_container_non_fixable_blk_read_error_propagated,wvblk_speres_in_parent, wvblk_rsrv_holes64, nfs_write_latency_average,wvsblk_vvrd_last_fbn, wvblk_saved_fsinfo_private_inos_used,fcp_read_data_rate, nfs_read_data_rate,cifs_protocol_other_latency_delta, clone_split_ra_lag,stream_sizes_hist_labels, synchronous_frees_rate,bad_container_non_fixable_blk_read_error_not_propagated,wv_fsinfo_blks_blks_rsrv_holes, cad_cli_deletes, clone_eio_blks,fcp_write_data, fcp_protocol_write_latency, flexcache_send_data_rate,flexcache_read_data_rate, nfs_protocol_write_latency_delta,wvblk_zombie_blks, asynchronous_frees_rate, wvblk_rsrv_holes_cifs,wvblk_saved_fsinfo_public_inos_total, wv_fsinfo_blks64_blks_rsrv_holes,cifs_other_ops_rate, cifs_protocol_read_latency_labels,bad_container_fixable_afs_blk_read_error_not_propagated,flexcache_read_ops_rate, clone_max_streams, san_write_latency,san_write_ops, wv_fsinfo_blks_res_state,wv_fsinfo_containment_version_highest_compression, nfs_write_data_rate,other_ops, cifs_write_data_rate, wvdf_last _fbn,iscsi_otherlatency_average, fcp_read_latency, fcp_write_latency,san_read_latency, bad_non_fixable_blk_read_error_not_propagated,wvblk_saved_fsinfo_private_inos_reserve,wv_fsinfo_containment_version_spare1,wv_fsinfo_containment_version_spare2, full_clone_latencies_hist,wvdf_max_frees, san_read_data, nfs_protocol_read_latency,wv_playlist_vvbn_holes, clone_sizes_hist, san_write_ops_rate,nfs_other_ops_rate, wvsblk_vvrd_vol_size,wvblk_saved_fsinfo_public_inos_used, iscsi_protocol_other_latency_delta,wvblk_rsrv_holes, cifs_protocol_write_latency_labels, iscsi_other_ops,wvol_number_suspended_rate, clone_inline_split_range_size_limitation,wvsnap_incore_count, cifs_other_ops, clone_inline_split_enospc,fcp_write_ops_rate, clone_snap_sub_file, clone_num_entries,total_protocol_other_latency_labels, wv_fsinfo_blks_rsrv_absents,san_write_latency_average, iscsi_protocol_write_latency_delta,synchronous_frees, wvblk_reclaim_time_start,total_protocol_read_latency_delta, cifs_protocol_write_latency,clone_afs_full_file, clone_inodes,wv_fsinfo_containment_version_highest_slc,fcp_protocol_other_latency_labels,wv_fsinfo_containment_version_highest_sle, wv_fsinfo_public_inos_used,vserver_name, nfs_write_latency, san_read_data_rate,full_clone_latencies_hist_labels, fcp_other_latency, cad_cli_updates,clone_max_entries, san_read_ops_rate, wvip_public_inofile_wi_blk_cnt,wv_fsinfo_private_inos_reserve, fcp_write_latency_average,wvip_vvol_container_wi_size, wv_fsinfo_blks_rsrv_parent,san_other_latency_average, wvdf_inconsistent_scores,bad_user_blk_read_error_propagated, cad_cli_inserts,flexcache_receive_data, clone_storage_blocks, wvbd_active_frees_y,cifs_read_data, cifs_write_ops_rate, wvblk_rsrv_absents,wvip_vvol_container_indirects, total_protocol_write_latency_delta,wv_playlist_hits, wvip_private_inofile_wi_blk_cnt, wvblk_past_eof64,fop_protocol_write_latency_labels, flexcache_write_ops_rate,iscsi_protocol_other_latency,bad_fixable_metafile_blk_read_error_propagated, nfs_read_data,bad_user_blkread_error_not_propagated, iscsi_other_ops_rate,fcp_protocol_read_latency, san_other_latency, read_data_rate,total_protocol_read_latency, total_protocol_other_latency,instance_uuid, fcp_other_ops_rate, cifs_write_data,cifs_protocol_read_latency_delta, internal_msgs, node_uuid,flexcache_write_data, read_blocks,wv_fsinfo_containment_version_compression, n s_other_ops,fcp_read_latency_average, nfs_protocol_write_latency,flexcache_read_data, clone_streams_efbig, iscsi_write_ops,clone_lookup_hits, nonzero_dbys_cnt,bad_fixable_blk_read_error_propagated, write_blocks, fcp_read_data,iscsi_write_latency, bad_zombie_ind_blk_read_err_propagated,write_latency, wv_volinfo_fs_options, read_blocks_rate, df worker,wv_fsinfo_containment_version_highest_spare2,wv_fsinfo_containment_version_highest_spare1, fcp_read_ops_rate,fcp_other_latency_average, wv_playlist_reqs,wv_fsinfo_containment_version_highest_vmalign,nfs_protocol_other_latency, wv_fsinfo_blks_overwrite_slider_pct,cifs_protocol_other_latency, wv_fsinfo_blks_snap_reserve_pct,iscsi_read_latency_average, iscsi_protocol_read_latency_labels,wv_fsinfo_containment_version_slc, wv_fsinfo_containment_version_sle,wv_fsinfo_private_inos_used, nfs_write_ops,iscsi_protocol_write_latency, wvsnap_ondisk_count,nfs_other_latency_average, repl_metafile_logical_xfer_checker_blks_cnt,clone_inline_split_edquot, wv_playlist_getbuf_failures,wvsblk_space_tax, read_latency, wv_fsinfo_private_inos_total,san_write_data_rate, wvbd_whole_frees, stream_sizes_hist,cifs_read_latency_average, flexcache_other_ops, flexcache_write_ops,wvdf_total_score, iscsi_protocol_other_latency_labels,wv_volinfo_fs_flags, cifs_other_latency,msgs_rejected_in_nvfailed_state, fcp_protocol_read_latency_delta,wv_fsinfo_blks_blks_rsrv_holes_cifs, wv_playlist_apfi_used_slots,cad_iron_removed, read_data, wvol_number_suspended, fcp_write_data_rate,wvdf_watermark, cifs_write_latency_average,clone_inline_split_kireeti_in_progress, clone_max_hierarchy,wv_fsinfo_public_inos_reserve, cad_crtime_updates, clone_inline_splits,vserver_uuid, wvblk_claim_time_abort, fcp_read_ops, wvsnap_loaded_total,flexcache_read_ops, flexcache_write_data_rate,wvblk_parent_to_be_reclaimed, total_ops, avg_latency,bad_non_fixable_blk_read_error_propagated, clone_unsplit_snap_entries,wvsblk_vvrd_flags, repl_metafile_logical_xfer_rebuild_buffer_blks_cnt,fcp_protocol_write_latency_delta,bad_container_fixable_afs_blk_read_error_propagated, san_other_ops_rate.

SYSTEM PERFORMANCE METRICS: write_ops_rate, total_ops_rate,read_ops_rate, sys_latency_hist_delta, node_name, fcp_data_sent,avg_processor_busy_percent, system_id, iscsi_ops, disk_data_read,cpu_busy, sys_latency_hist_labels, hdd_data_read_rate, num_processors,http_ops_rate, sys_read_latency_hist_labels, sys_read_latency_average,sys_avg_latency, hdd_data_written, nfs_ops,sys_write_latency_hist_labels, wafliron_delta, net_data_sent_rate,sys_avg_latency_average, hdd_data_written_rate, instance_uuid,nfs_ops_rate, fcp_data_recv, total_processor_busy,disk_data_written_rate, ssd_data_read, net_data_sent,fcp_data_recv_rate, cifs_ops_rate, ssd_data_written, total_ops,sys_latency_hist, fcp_data_sent_rate, hdd_data_read,disk_data_read_rate, sys_read_latency_hist, wafliron, http_ops,system_model, sys_write_latency_average, net_data_recv_rate,sys_read_latency, total_processor_busy_percent, wafliron_labels,node_uuid, serial_no, sys_write_latency, hostname, iscsi_ops_rate,cifs_ops, net_data_recv, instance_name, sys_write_latency_hist, fcp_ops,disk_data_written, cpu_elapsed_time, process_name, fcp_ops_rate,ssd_data_read_rate, avg_processor_busy, sys_write_latency_hist_delta,sys_read_latency_hist_delta, ontap_version, cpu_elapsed_time1,write_ops, cpu_elapsed_time2, read_ops, cpu_busy_percent,ssd_data_written_rate, uptime.

VFILER PERF METRICS: vfiler_read_ops, node_name, vfiler_write_ops,vfiler_net_data_sent, vfiler_misc_ops, vfiler_read_bytes_rate,vfiler_net_data_sent_rate, vfiler_net_data_recv_rate,vfiler_miscops_rate, vfiler_cpu_busy_percent, vfiler_cpu_busy_base,instance_uuid, vfiler_cpu_busy, vfiler_netdata_recv, vfiler_read_bytes,vfiler_write_ops_rate, node_uuid, vfiler_write_bytes, instance_name,vfiler_write_bytes_rate.process_name, vfiler_read_ops_rate.

QTREE PERF METRICS: parent_vol, internal_ops_rate, nfs_ops_rate,cifs_ops_rate, internal_ops, nfs_ops, cifs_ops, objname.

QUOTA PERF METRICS:, node_name, quota_lookups_labels,quota_name_db_blocks, quota_lookups, quota_db_blocks,quota_bplus_tree_blocks, instance_name, quota_disk_records_labels,instance_uuid, quota_types_labels, quota_records, node_uuid,quota_disk_records, quota_records_labels, quota_usermap_lookups_labels,process_name, quota_fsi_state, quota_types, quota_usermap_lookups,quota_state.

AGGREGATE PERF METRICS: blkr_async_no_msg_delta, cp_reads_hdd_rate,wvblk_saved_private_fsinfo_inos_total, wvblk_rsrv_overwrite,wv_fsinfo_containment_version_compression,blkr_blocks_redirected_reread_delta, blkr_wa_used_csc_aa_delta,blkr_redirect_blocks_ok_delta, blkr_wa_used_csc_aa,blkr_free_blocks_scanned_delta, bkr_blocks_redirected_noio_delta,blkr_segments_scanned_delta, wv_fsinfo_containment_version_spare1,wv_fsinfo_containment_version_spare2, user_write_blocks_hdd_rate,wvdf_max_frees, cp_reads_rate, blkr_redirect_demand_rereq,blkr_redirect_indirects_inspected_labels,blkr_blocks_redirected_noll_delta, wvblk_child_delalloc,total_transfers_rate, blkr_futsegments_scanned_delta,wvblk_child_pledge_percent, blkr_rejected_segments_in_current_aa_delta,wvblk_saved_public_fsinfo_inos_used, total_transfers,wvblk_rsrv_child_holes, blkr_non_csc_used_empty_delta,total_transfers_hdd, blkr_csc_empty_aa, wv_volinfo_fs_options,blkr_aggrsnap_blocks_scanned, wvblk_rsrv_holes,blkr_blocks_redirected_delta, wvbk_past_eof,blkr_rejected_segments_scanned, user_reads_hdd_rate,user_read_blocks_rate, wv_fsinfo_ssdblks_used,blkr_blocks_dummy_read_delta, delete_log,blkr_policy1_reject_reasons_labels,blkr_rejected_segments_before_scan_delta, user reads,blkr_redirect_blocks_invalid_delta, blkr_redirect_ra_11_delta, cp_reads,blkr_empty_segments_scanned, wvblk_lev0_over_nominal, process_name,blkr_rejected_blocks_scanned, ext_cache_ilog_full,wv_fsinfo_blks_blks_rsrv_holes, bkr_redirect_blocks_updated,wv_fsinfo_containment_version_highest_spare2,wv_fsinfo_containment_version_highest_spare1,bkrblocks_redirected_maybe_delta,blkr_redirect_indirects_updated_labels, blkr_blocks_redirected,cp_read_blocks_rate, delete_log_labels, user_writes_hdd,user_write_blocks_ssd, blkr_async_offline_delta, blkr_blocks_read_delta,blkr_csc_aa_requested, bkr_async_no_mem, blkr_blocks_scanned,wv_fsinfo_blks_overwrite_slider_pct, user_reads_rate,blkr_aggrsnap_blocks_scanned_delta, blkr_redirect_ra_1,bkr_redirect_ra_I0, wvblk_snap_reserve, bkr_redirect_blocks_ok,blkr_redirect_indirects_inspected, wv_fsinfo_blks_snap_reserve_pct,user_write_blocks_rate, wv_fsinfo_blks_rsrv_absents,wv_fsinfo_containment_version_slc, blkr_blocks_redirected_noio,wv_fsinfo_containment_version_highest_compression, wv_fsinfo_blks_total,wvbd_owner_changed_y, wv_fsinfo_containment_version_sle,wvblk_saved_private_fsinfo_inos_reserve, user_write_blocks_ssd_rate,wv_fsinfo_private_inos_used, bkr_csc_total_aa_cleaned, cp_reads_ssd,blkr_blocks_redirected_noverify, bkr_redirect_demand_req,blkr_blocks_redirected_noread_delta, bkr_csc_buf_suspended_delta,user_read_blocks_ssd_rate, blkr_rejected_blocks_scanned_delta,cp_reads_ssd_rate, blkr_async_launched, blkr_csc_empty_aa_delta,blkr_non_csc_used_empty, blkr_segments_scanned, blkr_blocks_dummy_read,wv_fsinfo_blks_blks_rsrv_overwrite, instance_name, wvbd_whole_frees_o,user_reads_ssd_rate, cp_reads_hdd,wv_fsinfo_containment_version_highest_slc, wv_fsid,bkr_wa_used_non_csc_aa, wv_fsinfo_containment_version_highest_sle,blkr_super_blocks_scanned, wv_fsinfo_public_inos_used,bkr_async_completed_delta, bkr_reads_launched_delta,blkr_blocks_reallocated_delta, blkr_csc_msg_failed_delta,user_reads_ssd, blkr_rejected_segments_before_scan, blkr_csc_full_aa,blkr_redirect_indirects_ok_delta, bkr_policy1_reject_reasons,wvbd_whole_frees, blkr_redirect_kireetis_scanned_delta,user_read_blocks_hdd_rate, cp_read_blocks_ssd,blkr_policy1_reject_reasons_delta, wvblk_child_indirect_blk_cnt,blkr_redirect_indirects_ok_labels, node_name,blkr_csc_total_aa_cleaned_delta, blkr_non_csc_used_full_delta,wv_fsinfo_blks_used, wv_fsinfo_ssdblks_total, wv_fsinfo_fs_version,disk_type, blkr_redirect_demand_rereq_delta,wv_fsinfo_public_inos_total, user_write_blocks_hdd, cp_read_blocks,blkr_rejected_segments_scanned_delta, blkr_redirect_ra_I0_delta,cp_read_blocks_ssd_rate, wv_volinfo_fs_flags,bkr_blocks_redirected_noverify_delta, blkr_blocks_redirected_maybe,parent_host, wvblk_rsrv_holes64, wvblk_rsrv_child_overwrite,blkr_csc_aa_requested_delta, wvip_public_inofile_wi_blk_cnt,blkr_blocks_postfiltered_delta, blkr_blocks_postfiltered_labels,wv_fsinfo_blks_blks_rsrv_holes_cifs, wvblk_space_tax,blkr_async_launched_delta, blkr_csc_aa_inventory,blkr_wa_used_non_csc_aa_delta, wvblk_saved_private_fsinfo_inos_used,blkr_async_completed, wv_fsinfo_private_inos_reserve,blkr_blocks_reallocated, blkr_async_no_msg,blkr_blocks_redirected_nomem_delta, wvbd_active_frees,blkr_blocks_redirected_nomem, wv_fsinfo_blks_reserve, user_reads_hdd,wv_fsinfo_ssdblks_used_to_write_cache, blkr_free_blocks_scanned,blkr_reads_launched, blkr_redirect_indirects_inspected_delta,blkr_blocks_postfiltered, blkr_csc_buf_suspended,blkr_aa_blocks_scanned_delta, blkr_blocks_overwritten,blkr_csc_msg_completed, total_transfers_hdd_rate, blkr_async_offline,blkr_super_blocks_scanned_delta, wvbd_active_frees_y,blkr_redirect_demand_req_delta, blkr_csc_msg_completed_delta,blkr_csc_msg_failed, user_writes_ssd_rate, blkr_rejected_ssd_rgs,blkr_redirect_kireetis_scanned, blkr_redirect_indirects_ok,wv_fsinfo_public_inos_reserve, blkr_blocks_redire ed_nol1,wvblk_zombie_blks, blkr_blocks_redirected_reread, user_read_blocks_hdd,blkr_redirect_ra_map_delta, wvblk_past_eof64, blkr_blocks_read,blkr_redirect_susps, wvblk_rsrv_holes_cifs,blkr_redirect_indirects_updated_delta, wv_vol_type,wv_fsinfo_blks64_blks_rsrv_holes, wvzmb_num_zmsgs_inuse,wvblk_saved_public_fsinfo_inos_reserve, cp_read_blocks_hdd_rate,blkr_async_no_mem_delta, blkr_blocks_overwritten_delta,wvblk_rsrv_holes_cifs64, blkr_rejected_ssd_rgs_delta,total_transfers_ssd_rate, wv_fsinfo_ssdblks_used_by_plane0,instance_uuid, blkr_redirect_indirects_updated, user_read_blocks_ssd,user_write_blocks, blkr_full_segments_scanned,wvblk_rsrv_child_overwrite_always, blkr_blocks_scanned_delta,blkr_non_csc_used_full, user_writes, blkr_blocks_redirected_noread,user_writes_ssd, blkr_redirect_demand_drop_delta, node_uuid,wvblk_saved_public_fsinfo_inos_total, wv_fsinfo_blks_res_state,wv_fsinfo_private_inos_total, blkr_redirect_demand_drop,blkr_rejected_segments_in_current_aa, wvblk_child_to_be_reclaimed,user_writes_rate, blkr_redirect_ra_map, wv_fsinfo_blks_used_by_plane0,cp_read_blocks_hdd, blkr_redirect_blocks_invalid,blkr_redirect_blocks_updated_delta, user_writes_hdd_rate,blkr_redirect_susps_delta, blkr_empty_segments_scanned_delta,blkr_aa_blocks_scanned, total_transfers_ssd, wv_fsinfo_blkr_cp,user_read_blocks, wv_fsinfo_blks64_blks_rsrv_holes_cifs.

DISK PERF METRICS: disk_busy_percent, node_name, cp_reads_rate,user_write_chain, user_read_latency_average, user_reads_rate,total_transfers_rate, user_read_chain_average, guarenteed_write_chain,user_read_blocks_rate, raid_group, skip_blocks_rate,cp_read_chain_average, guarenteed_read_blocks, cp_read_blocks,guarenteed_write_latency_average, disk_io_latency_histogram,guarenteed_read_blocks_rate, raid_type, io_pending,guarenteed_write_latency, guarenteed_write_blocks,user_writes_in_skip_mask_rate, dlsched_distant, cp_read_latency,total_transfers, io_queued_average, user_write_latency_average,disk_capacity, user_read_chain, instance_uuid, raid_group_id,user_read_latency, user_write_blocks, dlsched_max_distant,dlsched_immediate, disk busy, user_skip_write_ios_rate,dlsched_count_rate, guarenteed_read_chain, user_writes_in_skip_mask,user writes, display_name, guarenteed_read_chain_average,io_pending_average, user_write_latency, guarenteed_reads_rate,node_uuid, guarenteed_write_chain_average, dlsched_max_background,dlsched_count, cp_read_chain, guarenteed_write_blocks_rate, user_reads,guarenteed_reads, skip_blocks, instance_name,disk_io_latency_histogram_labels, cp_reads, user_writes_rate,process_name, guarenteed_writes, dlsched_wait_average,user_write_chain_average, raid_name, base_for_disk_busy,guarenteed_writes_rate, user_write_blocks_rate, dlsched_wait,disk_io_latency_histogram_delta, cp_read_latency_average,cp_read_blocks_rate, userskip_write_ios, disk speed,guarenteed_read_latency, dlsched_io_time, io_queued, user_read_blocks,guarenteed_read_latency_average, objtype.

LUN PERF METRICS: read_data_rate, read_align_histo, write_ops,write_data, scsi_partner_data_rate, avg_other_latency,write_partial_blocks, queue_full, display_name,avg_other_latency_average, total_ops_rate, read_data, read_ops_rate,scsi_partner_ops, write_align_histo, avg_write_latency_average,scsi_partner_ops_rate, read_partial_blocks_percent, read_ops1,read_align_histo_labels, read_ops, avg_write_latency,avg_read_latency_average, total_ops, queue_full_rate,read_align_histo_percent, write_align_histo_percent,read_partial_blocks, write_ops1, queue_depth_lun, other ops,write_partial_blocks_percent, avg_latency, write_data_rate,write_ops_rate, avg_read_latency, avg_latency_average, other_ops_rate,scsi_partner_data.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of the foregoing methods.

In an embodiment, a non-transitory computer readable storage medium,storing software instructions, which when executed by one or moreprocessors cause performance of any of the foregoing methods.

4. Implementation Mechanism—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustomizable hard-wired logic, ASICs, or FPGAs with customizableprogramming to accomplish the techniques. The special-purpose computingdevices may be desktop computer systems, portable computer systems,handheld devices, televisions, wearable computing devices, networkingdevices or any other device that incorporates hard-wired and/or programlogic to implement the techniques.

For example, FIG. 6 is a block diagram that illustrates a computersystem 600 upon which an embodiment of the invention may be implemented.Computer system 600 includes a bus 602 or other communication mechanismfor communicating information, and a hardware processor 604 coupled withbus 602 for processing information. Hardware processor 604 may be, forexample, a general purpose microprocessor.

Computer system 600 also includes a main memory 606, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 602for storing information and instructions to be executed by processor604. Main memory 606 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 604. Such instructions, when stored innon-transitory storage media accessible to processor 604, rendercomputer system 600 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 600 further includes a read only memory (ROM) 608 orother static storage device coupled to bus 602 for storing staticinformation and instructions for processor 604. A storage device 610,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 602 for storing information and instructions.

Computer system 600 may be coupled via bus 602 to a display 612, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 6a, including alphanumeric and other keys, is coupled tobus 602 for communicating information and command selections toprocessor 604. Another type of user input device is cursor control 616,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 604 and forcontrolling cursor movement on display 612. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

In some embodiments, a customer interacts with computer system 600 viatouch, for example, by tapping or gesturing over certain locations. Adisplay screen of display 612 may also be capable of detecting touch.

Computer system 600 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 600 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 600 in response to processor 604 executing one or more sequencesof one or more instructions contained in main memory 606. Suchinstructions may be read into main memory 606 from another storagemedium, such as storage device 610. Execution of the sequences ofinstructions contained in main memory 606 causes processor 604 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 610. Volatile media includes dynamic memory, such asmain memory 606. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 602. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 604 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 600 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 602. Bus 602 carries the data tomain memory 606, from which processor 604 retrieves and executes theinstructions. The instructions received by main memory 606 mayoptionally be stored on storage device 610 either before or afterexecution by processor 604.

Computer system 600 also includes a communication interface 618 coupledto bus 602. Communication interface 618 provides a two-way datacommunication coupling to a network link 620 that is connected to alocal network 622. For example, communication interface 618 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 618 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 618sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 620 typically provides data communication through one ormore networks to other data devices. For example, network link 620 mayprovide a connection through local network 622 to a host computer 624 orto data equipment operated by an Internet Service Provider (ISP) 626.ISP 626 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 628. Local network 622 and Internet 628 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 620and through communication interface 618, which carry the digital data toand from computer system 600, are example forms of transmission media.

Computer system 600 can send messages and receive data, includingprogram code, through the network(s), network link 620 and communicationinterface 618. In the Internet example, a server 630 might transmit arequested code for an application program through Internet 628, ISP 626,local network 622 and communication interface 618.

The received code may be executed by processor 604 as it is received,and/or stored in storage device 610, or other non-volatile storage forlater execution.

5. Extensions and Alternatives

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the embodiments, and what isintended by the applicants to be the scope of the embodiments, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

In drawings, various system components are depicted as beingcommunicatively coupled to various other components by arrows. Thesearrows illustrate only certain examples of information flows between thecomponents of the depicted systems. Neither the direction of the arrowsnor the lack of arrow lines between certain components should beinterpreted as indicating the absence of communication between thecertain components. Indeed, each component of the depicted systems mayfeature an open port, API, or other suitable communication interface bywhich the component may become communicatively coupled to othercomponents of the depicted systems as needed to accomplish any of thefunctions of the systems described herein.

1. A method comprising: obtaining, by a computer system from a firstcomponent on a computer network, virtual machine data related to aparticular virtual machine, the virtual machine data identifying aparticular storage volume configured for use by the particular virtualmachine; obtaining, by the computer system from a second component onthe computer network, storage volume data indicative of a performance ofthe particular storage volume; determining, by the computer system, thatthe storage volume data are related to the virtual machine data, basedon information in the virtual machine data and information in thestorage volume data; and causing, by the computer system, display of agraphical user interface that includes a visual association of virtualmachine information related to the particular virtual machine withstorage volume information related to the particular storage volume,based on a result of said determining.
 2. The method of claim 1, whereinthe graphical user interface includes a visual association of virtualmachine information related to the particular virtual machine withperformance information related to the particular storage volume.
 3. Themethod of claim 1, wherein the graphical user interface includes a usercontrol associated with the particular storage volume, and whereinselection by a user of the user control associated with the particularstorage volume causes display of performance information related to theparticular storage volume.
 4. The method of claim 1, wherein: thegraphical user interface includes a visual association of virtualmachine information related to the particular virtual machine withperformance information related to the particular storage volume; thegraphical user interface includes a user control associated with theparticular storage volume; and selection by a user of the user controlassociated with the particular storage volume causes display ofperformance information related to the particular storage volume.
 5. Themethod of claim 1, wherein the virtual machine data is obtained by thecomputer system from a first application programming interface (API)made available by a virtual system manager that manages one or morevirtual machines, and wherein the storage volume data is obtained by thecomputer system from a second API made available by a storage managerthat manages one or more storage units and that is not the virtualsystem manager.
 6. The method of claim 1, further comprising:determining performance of a storage controller that manages theparticular volume based on storage data obtained from the storagecontroller, wherein the storage volume information includes informationthat indicates performance of the storage controller.
 7. The method ofclaim 1, further comprising: obtaining the virtual machine data from avirtual machine information index; and obtaining the storage data from aobtaining the virtual machine data from a virtual machine informationindex.
 8. The method of claim 1 further comprising: determining aperformance metric associated with the particular storage volume; andcausing display of the performance metric associated with the particularstorage volume and an indication that the particular storage volume is acause of a performance issue associated with the particular virtualmachine.
 9. The method of claim 1, further comprising: determining aperformance metric associated with the particular virtual machine; andcausing display of a visualization of the performance metric.
 10. Themethod of claim 1, further comprising: changing a state of theperformance metric based on an amount of remaining storage space; andcausing display of a visualization of the performance metric.
 11. Themethod of claim 1, further comprising: determining a performance metricassociated with the particular storage volume; changing a state of theperformance metric based on an amount of storage space of the storagevolume that is over-provisioned; and causing display of a visualizationof the performance metric.
 12. The method of claim 1, furthercomprising: determining a performance metric associated with theparticular virtual machine, based on memory that is actively in use inthe virtual machine; and causing display of a visualization of theperformance metric.
 13. The method of claim 1, further comprising:determining a performance metric associated with the particular virtualmachine, based on a virtual memory saved by memory sharing; and causingdisplay of a visualization of the performance metric.
 14. The method ofclaim 1, further comprising: determining a performance metric associatedwith the particular virtual machine, based on a virtual memory used forthe virtual machine; and causing display of a visualization of theperformance metric.
 15. The method of claim 1, further comprising:determining a performance metric associated with the particular virtualmachine, based on physical memory that is mapped to the particularvirtual machine, which precludes overhead memory; and causing display ofa visualization of the performance metric.
 16. The method of claim 1,further comprising: determining a performance metric associated with theparticular virtual machine, based on an amount of physical memory thatis reclaimed by a host of the particular virtual machine through aballooning driver; and causing display of a visualization of theperformance metric.
 17. The method of claim 1, further comprising:determining a performance metric associated with the particular virtualmachine, based on memory that is read by the virtual machine from a swapfile of a host of the particular virtual machine; and causing display ofa visualization of the performance metric.
 18. The method of claim 1,further comprising: determining a performance metric associated with theparticular virtual machine, based on an amount of memory that theparticular virtual machine has had to write to a swap file; and causingdisplay of a visualization of the performance metric.
 19. The method ofclaim 1, further comprising: determining a performance metric associatedwith the particular virtual machine, based on an amount of memory of thevirtual machine that has been swapped by a host of the virtual machine:and causing display of a visualization of the performance metric. 20.The method of claim 1, further comprising: determining a performancemetric associated with the particular virtual machine, wherein theperformance metric is indicative of any of a task assignment count, atask assignment type, a task completion count_(;) or migrationsassociated with the virtual machine or a host of the virtual machine;and causing display of a visualization of the performance metric. 21.The method of claim 1, further comprising: determining a performancemetric associated with the particular virtual machine, wherein theperformance metric describes a property of a virtual environmentincluding the virtual machine, a host of the virtual machine, a virtualmachine manager_(;) and a virtual system manager.
 22. The method ofclaim 1 further comprising: causing generation of a visualization of aperformance metric in a graphical interface that enables a user tovisually determine a cause of a performance issue of the particularvirtual machine.
 23. The method of claim 1 further comprising: causinggeneration of a visualization of a value associated with a performancemetric in a graphical interface, the visualization being indicative of acause of a performance issue of the particular virtual machine.
 24. Themethod of claim 1, further comprising: determining a performance metricassociated with the particular virtual machine, wherein the performancemetric is a type of computer cluster performance metric; and causingdisplay of a visualization of the performance metric.
 25. The method ofclaim 1, further comprising: determining a performance metric associatedwith the particular virtual machine, wherein the performance metric is ahost-based replication performance metric; and causing display of avisualization of the performance metric.
 26. The method of claim 1,further comprising: determining a memory performance metric associatedwith the particular virtual machine; and causing display of avisualization of the performance metric.
 27. The method of claim 1,further comprising: determining a network performance metric associatedwith the particular virtual machine; and causing display of avisualization of the performance metric.
 28. The method of claim 1,further comprising: determining a power performance metric associatedwith the particular virtual machine; and causing display of avisualization of the performance metric.
 29. A processing systemcomprising: a processor; a network interface coupled to the processor;and a memory including instructions, execution of which by the processorcauses the processing system to: obtain, from a first component on acomputer network, virtual machine data related to a particular virtualmachine, the virtual machine data identifying a particular storagevolume configured for use by the particular virtual machine; obtain,from a second component on the computer network, storage volume dataindicative of a performance of the particular storage volume; determinethat the storage volume data are related to the virtual machine data,based on information in the virtual machine data and information in thestorage volume data; and cause display of a graphical user interfacethat includes a visual association of virtual machine informationrelated to the particular virtual machine with storage volumeinformation related to the particular storage volume, based on a resultof said determining.
 30. A non-transitory storage medium storinginstructions, execution of which in a processing system causes theprocessing system to perform operations that comprise: obtaining, from afirst component on a computer network, virtual machine data related to aparticular virtual machine, the virtual machine data identifying aparticular storage volume configured for use by the particular virtualmachine; obtaining, from a second component on the computer network,storage volume data indicative of a performance of the particularstorage volume; determining that the storage volume data are related tothe virtual machine data, based on information in the virtual machinedata and information in the storage volume data; and causing display ofa graphical user interface that includes a visual association of virtualmachine information related to the particular virtual machine withstorage volume information related to the particular storage volume,based on a result of said determining.